MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS OF SPIKING …eprints.utm.my/id/eprint/77783/1/AbdulRazakYahyaPFC2015.pdf · Rangkaian Neural Pakuan (SNN) dianggap sebagai generasi ketiga

MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS OF

SPIKING NEURAL NETWORKS

ABDULRAZAK YAHYA SALEH

UNIVERSITI TEKNOLOGI MALAYSIA

iv

MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS OF

SPIKING NEURAL NETWORKS

ABDULRAZAK YAHYA SALEH

A thesis submitted in fulfilment of the

requirements for the award of the degree of

Doctor of Philosophy (Computer Science)

Faculty of Computing

Universiti Teknologi Malaysia

JUNE 2015

vi

To my beloved parents, wife, children, brothers and my sisters

vii

ACKNOWLEDGEMENT

Firstly, All my praise and thanks are owed to Allah, who honored me the

health and persistence who substantially depends on Him.

I am very grateful to my main supervisor, Prof. Dr. Siti Mariyam

Shamsuddin. I wish to express my sincere appreciation to her for all her kind

guidance and inspiration to make this research possible. Her personality, enthusiasm,

patience and intellectual spirit made her a great supervisor and invaluable role model

for my professional career.

I am also grateful to my co-supervisor Dr. Haza Nuzly Bin Abdull Hamed for

his precious advices and comments and knowledge sharing in spiking neural

network. Special thanks for his generous help throughout the duration of this study.

Many thanks to the Ministry of Higher Education (MOHE) under the research

grant that have partially paid from LRGS Grantt R.J130000.7828.4L805 - MULTI-

VARIATE PATTERN ANALYSIS FOR FORENSIC IDENTIFICATION AND

VERIFICATION for the support during my study.

In addition, I am extremely grateful to my family for unlimited support and

encouragement during this research. My sincere appreciation also extends to UTM

Big Data Center, Soft Computing Research Group (SCRG) and all my colleagues for

the support and incisive comments in making this study a success. Their views and

tips are useful indeed. Unfortunately, it is not possible to list all of them in this

limited space.

viii

ABSTRACT

Spiking neural network (SNN) is considered as the third generation of artificial

neural networks. Although there are many models of SNN, Evolving Spiking Neural

Network (ESNN) is widely used in many recent research works. Among the many important

issues that need to be explored in ESNN are determining the optimal pre-synaptic neurons

and parameters values for a given data set. Moreover, previous studies have not investigated

the performance of the multi-objective approach with ESNN. In this study, the aim is to find

the optimal pre-synaptic neurons and parameter values for ESNN simultaneously by

proposing several integrations between ESNN and differential evolution (DE). The proposed

algorithms applied to address these problems include DE with evolving spiking neural

network (DE-ESNN) and DE for parameter tuning with evolving spiking neural network

(DEPT-ESNN). This study also utilized the approach of multi-objective (MOO) with ESNN

for better learning structure and classification accuracy. Harmony Search (HS) and memetic

approach was used to improve the performance of MOO with ESNN. Consequently, Multi-

Objective Differential Evolution with Evolving Spiking Neural Network (MODE-ESNN),

Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural

Network (HSMODE-ESNN) and Memetic Harmony Search Multi-Objective Differential

Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) were applied to

improve ESNN structure and accuracy rates. The hybrid methods were tested by using seven

benchmark data sets from the machine learning repository. The performance was evaluated

using different criteria such as accuracy (ACC), geometric mean (GM), sensitivity (SEN),

specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and

average site performance (ASP) using k-fold cross validation. Evaluation analysis shows

that the proposed methods demonstrated better classification performance as compared to the

standard ESNN especially in the case of imbalanced data sets. The findings revealed that the

MEHSMODE-ESNN method statistically outperformed all the other methods using the

different data sets and evaluation criteria. It is concluded that multi objective proposed

methods have been evinced as the best proposed methods for most of the data sets used in

this study. The findings have proven that the proposed algorithms attained the optimal pre-

synaptic neurons and parameters values and MOO approach was applicable for the ESNN.

ix

ABSTRAK

Rangkaian Neural Pakuan (SNN) dianggap sebagai generasi ketiga buatan

saraf rangkaian.Walaupun terdapat banyak model dari SNN, Rangkaian Neural

Pakuan Berevolusi (ESNN) telah digunakan secara meluas di dalam kajian terkini.

Antara isu-isu penting yang perlu dikaji di dalam ESNN adalah menentukan bilangan

optimal pra-sinaptik neuron dan bilangan parameter bagi data set yang telah

diberikan. Selain itu, kajian sebelum ini tidak menekankan prestasi pendekatan

pelbagai objektif bersama ESNN. Tujuan utama kajian ini adalah untuk mencari

nilai optimum pra-sinaptik neuron dan parameter ESNN secara serentak dengan

mencadangkan beberapa integrasi antara ESNN dan Evolusi Pembezaan (DE).

Pelbagai algoritma telah dicadangkan iaitu DE bersama Rangkaian Neural Pakuan

Berevolusi (DE-ESNN) dan DE bagi penalaan parameter dengan Rangkaian Neural

Pakuan Berevolusi (DEPT-ESNN). Kajian ini juga menggunakan pendekatan multi-

objektif (MOO) bersama ESNN, bagi menerangkan struktur pembelajaran yang lebih

baik dan ketepatan pengelasan. Carian Harmoni (HS) dan pendekatan memetik

digunakan untuk meningkatkan prestasi MOO bersama ESNN. Oleh itu, Multi-

Objektif Evolusi Pembezaan bersama Rangkaian Neural Pakuan Berevolusi (MODE-

ESNN), Carian Harmoni Multi-Objektif Evolusi Pembezaan bersama Rangkaian

Neural Pakuan Berevolusi (HSMODE-ESNN) dan Multi-Objektif Evolusi

Pembezaan bersandarkan memetik harmoni bersama Rangkaian Neural Pakuan

Berevolusi (MEHSMODE-ESNN) digunakan bagi memperbaiki struktur ESNN dan

ketetapan kadar. Kaedah hibrid telah diuji dengan menggunakan tujuh penanda aras

data set dari repositori pembelajaran mesin. Prestasi kaedah yang dicadangkan telah

dinilai menggunakan kriteria yang berbeza seperti ketepatan (ACC), min geometri

(GM), kepekaan (SEN), kekhususan (SPE), nilai ramalan positif (PPV), nilai ramalan

negatif (NPV) dan tapak purata prestasi (ASP) menggunakan pengesahan silang k

kali ganda. Analisis penilaian menunjukkan bahawa kaedah yang dicadangkan

menunjukkan prestasi klasifikasi yang lebih baik berbanding dengan standard ESNN

terutama dalam kes data set yang tidak seimbang. Penilaian ini mendedahkan bahawa

kaedah MEHSMODE-ESNN statistik secara telak mengatasi semua kaedah lain

yang menggunakan data set dan kriteria penilaian yang berbeza. Hasil kajian

mendapati bahawa MEHSMODE-ESNN yang di cadangkan telah terbukti sebagai

kaedah terbaik bagi kebanyakan data set yang digunakan dalam kajian ini. Hasil

kajian telah membuktikan bahawa algoritma yang dicadangkan mencapai optimum

neuron pra-sinaptik dan parameter nilai dan pendekatan MOO terpakai untuk ESNN.

x

TABLE OF CONTENTS

PAGE TITLE CHAPTER

v DECLARATION

vi DEDICATION

vii ACKNOWLEDGEMENT

viii ABSTRACT

ix ABSTRAK

x TABLE OF CONTENTS

xiv LIST OF TABLES

xviii LIST OF FIGURES

xxiv LIST OF APPREVIATIONS

xxvii LIST OF APPENDICES

1 INTRODUCTION 1

1 1.1 Overview

3 1.2 Problem Background

6 1.3 Problem Statement

8 1.4 Research Aim

9 1.5 Research Objectives

9 1.6 Research Scope

10 1.7 Research Methodology Overview

12 1.8 Summary of Research Contributions

13 1.9 Thesis Outline

xi

15 LITERATURE REVIEW 2

17 2.1 Overview of Spiking Neural Network

17 2.1.1 Introduction to SNN

18 2.1.2 Neuron models

19 2.1.2.1 Hodgkin-Huxley model

20 2.1.2.2 Izhikevich SNN model

22 2.1.2.3 Leaky-integrate-and-fire model

22 2.1.2.4 Spike response model ( SRM )

23 2.1.2.5 Thorpe model

23 2.1.3 Neuronal coding

23 2.1.3.1 Temporal coding

24 2.1.3.2 Rate coding

24 2.1.3.3 Population coding

25 2.1.4 Learning methods

25 2.1.4.1 Unsupervised learning

26 2.1.4.2 Supervised learning

26 2.1.5 SNN types/architectures

26 2.1.5.1 ESNN

29 2.1.5.2 SpikeProp

30 2.1.5.3 Spiking -timing dependent

plasticity (STDP)

30 2.1.5.4 Spatio-temporal pattern

recognition

31 2.2 Multi-Objective Evolutionary Algorithms (EAs)

Optimization

31 2.2.1 Multi-objective optimization (MOO)

32 2.2.1.1 Definition of a MOO problem

33 2.2.1.2 Search and decision making

34 2.2.1.3 Methods of MOO algorithms

35 2.2.2 EA algorithms

36 2.2.2.1 Differential Evolution (DE)

39 2.2.2.2 Harmony Search (HS)

44 2.2.2.3 Memetic Technique

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45 2.2.3 Discussion of classification enhancement

45 2.3 Related Work and Discussion

53 2.4 Summary

55 RESEARCH METHODOLOGY 3

55 3.1 Introduction

55 3.2 General Research Framework

58 3.2.1 Phase1:Research Design

58 3.2.1.1 Improvement of Overall Research

Plan

58 3.2.1.2 Improvement of operational

framework and algorithms for

proposed methods

63 3.2.2 Phase2: Experimental Design

63 3.2.2.1 Data set Preparation

69 3.2.2.2 Learning Phase

69 3.2.3 Phase3: Results Evaluation and Comparison

69 3.2.3.1 Performance Measures

72 3.2.3.2 Statistical test

72 3.3 Summary

73

PROPOSED HYBRID METHODS FOR

EVOLVING SPIKING NEURAL NETWORK

4

73 4.1 Introduction

74 4.2 Enhancement of ESNN Structure by Differential

Evolution (DE-ESNN) for Pre-Synaptic Neurons

77 4.3 Parameter Optimization of DE-ESNN

(DEPT-ESNN)

80 4.4 Experimental Study

80 4.4.1 Experimental design

81 4.4.2 Analysis of the proposed methods using

k- fold cross-validation

82 4.4.2.1 Results and analysis of ESNN

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85 4.4.2.2 Results and analysis of DE-ESNN

87 4.4.2.3 Results and analysis of DEPT-

ESNN

91 4.5 Analysis of the Hybrid Proposed Methods Based

on the Convergence Time

98 4.6 Statistical Analysis of the Proposed Hybridisation

Models with ESNN

99 4.7 Summary

101

MULTI-OBJECTIVE HYBRID METHODS FOR

EVOLVING SPIKING NEURAL NETWORK

5

101 5.1 Introduction

102 5.2 Multi-Objective DE based ESNN (MODE-ESNN)

104 5.3 Enhancement of Multi-Objective Based HS

(HSMODE-ESNN)

108 5.4 Enhancement of Multi-Objective Based Memetic

Method (MEHSMODE-ESNN)

110 5.5 Experimental Study

111 5.5.1 Experimental design

111 5.5.2 Analysis of the proposed methods using

k-fold cross-validation

112 5.5.2.1 Results and Analysis of

MODE- ESNN

115 5.5.2.2 Results and analysis of

HSMODE-ESNN

118 5.5.2.3 Results and analysis of

MEHSMODE-ESNN

123 5.6 Analysis of the MOO Methods with ESNN Based

on the Convergence Time


Models with ESNN

130 5.8 Summary

xiv

132 COMPARATIVE STUDY OF THE PROPOSED

METHODS

6


133 6.2 Analysis of the Proposed Methods on the Single

and Multi-Objective Hybrid

153 6.3 Analysis of the Proposed Methods with Several

Data Mining Algorithms

158 6.4 Analysis of the Proposed Methods with Various

Classifiers

168 6.5 Analysis of the Proposed Methods Based on

Computational Time and Convergence Time

169 6.5.1 Analysis of the proposed methods based on

computational time

170 6.5.2 Comparison with related works based on

computational time


Models with ESNN

173 6.7 Summary

174 CONCLUSION AND FUTURE WORK 7


174 7.2 Thesis Summary

176 7.3 Research Contributions

177 7.4 Future Work

179 REFERENCES

191 -198 Appendix A

xiv

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Hodgkin-Huxley parameters (Gerstner and Kistler, 2002) 20

2.2 Description of parameters of Izhikevich model 21

2.3 Description of parameters of the HSA algorithm 41

2.4 Summary of fashionable approaches in SNN research 46

2.5 Summary of review on SNNs 50

2.6 Framework of the study 57

3.1 Overall research plan 59

3.2 Summary of data sets which used in this study 66

4.1 Parameter settings for the proposed algorithms 81

4.2 Results of training error, testing error and value of parameters

for ESNN 82

4.2 ( continued) 83

4.3 Results of SEN, SPE, GM and ACC for ESNN 83

4.4 Results of NPV, PPV and ASP for ESNN 84


for DE-ESNN 85

4.6 Results of SEN, SPE, GM and ACC for DE-ESNN 86

4.7 Results of NPV, PPV and ASP for DE-ESNN 87

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for DEPT-ESNN 88

4.9 Results of SEN, SPE, GM and ACC for DEPT-ESNN 89

4.10 Results of NPV, PPV and ASP for DEPT-ESNN 90

4.11 Friedman descriptive statistics of the hybrid proposed

algorithms 99

4.12 Friedman test statistics of the hybrid proposed algorithms 99

5.1 Parameter settings for MOO proposed algorithms 111

5.2 Results of training error, testing error and number of

parameters for MODE-ESNN 112

5.2 (continued) 113

5.3 Results of SEN, SPE, GM and ACC for MODE-ESNN 114

5.4 Results of NPV, PPV and ASP for MODE-ESNN 115


parameters for HSMODE-ESNN 116

5.6 Results of SEN, SPE, GM and ACC for HSMODE-ESNN 117

5.7 Results of NPV, PPV and ASP for HSMODE-ESNN 118


parameters for MEHSMODE-ESNN 119

5.9 Results of SEN,SPE,GM and ACC for MEHSMODE-ESNN 120

5.10 Results of NPV, PPV and ASP for MEHSMODE-ESNN 121

5.11 Friedman descriptive statistics of MOO proposed algorithms 130

5.12 Friedman test statistics of the MOO proposed algorithms 130

6.1 Comparison of results of all proposed algorithms in terms of

the modulation factor parameter ( Mod) for 10-fold cross-

validation 134

xvi


the similarity value parameter (Sim) for ten-fold cross-

validation 135


the proportion factor parameter (Threshold) for ten-fold

cross-validation 137


the ESNN structure (pre-synaptic neurons) for ten-fold cross-

validation 138

6.5 Sensitivity analysis of all proposed algorithms for ten-fold


6.6 SPE analysis for all proposed methods for ten-fold cross-

validation 141

6.7 GM analysis for all proposed methods for ten-fold cross-

validation 143

6.8 Accuracy analysis for all proposed methods for ten-fold


6.9 NPV analysis for all proposed methods for ten-fold cross-

validation 145

6.10 PPV analysis for all proposed methods for ten-fold cross-

validation 146

6.11 ASP analysis for all proposed methods for ten-fold cross-

validation 147

6.12 Summary analysis of all proposed methods 148

6.12 (Continued) 149

6.13 Description In-depth of data sets which used in this study 152

6.14 Summary of performance evaluation on the proposed

methods for each data sets 152

6.15 Description of KEEL data mining algorithm (Alcalá et al.,

2011) 154

xvii

6.16 Results of accuracy of training performance 154

6.17 Results of accuracy of testing performance 155

6.18 Results of different measures of classification performance of

all proposed methods with some standard classifiers 159



6.19 Computational time (in seconds) for training the proposed

algorithms 169

6.20 Computational time (in seconds) for testing the proposed

algorithms 169

6.21 Summary of computational time the proposed and existing

methods 170


6.22 Friedman descriptive statistics of the hybrid proposed

algorithms 173

6.23 Friedman test statistics of the hybrid proposed algorithms 173

xviii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

1.1 Scenario guides to the research problem 5

1.2 Flow of research methodology phases 11

1.3 Summary of research contributions 12

2.1 A general overview of the literature review of this study 16

2.2 A schematic representation of SNN. Redrawn from (Gerstner

and Kistler, 2002) 18

2.3 Diagram of Hodgkin-Huxley model(Gerstner and Kistler,

2002). 19

2.4 Parameters of Izhikevich model (Izhikevich, 2010). 21

2.5 Diagram of the LIF model(Bishop and Maass, 1999) 22

2.6 GRF encoding. (Meftah et al., 2010) 25

2.7 A simplified architecture of ESNN (Hamed et al., 2009a) 28

2.8 Flowchart for training of DE (Storn and Price, 1997) 38

2.9 Pseudo-code of standard HSA 40

2.10 Flowchart for training of HS 43

2.11 Pseudo-code of MA(Elbeltagi et al., 2005) 44

3.1 Framework of the study 57

3.2 Schematic of the proposed methods 62

3.3 N-fold cross-validation 68

4.1 Visual summary of the hybrid proposed methods 74

file:///C:/Users/sony/Desktop/RAZAK_CORRECTION%20FILE%2028%20APRIL.docx%23_Toc417979036file:///C:/Users/sony/Desktop/RAZAK_CORRECTION%20FILE%2028%20APRIL.docx%23_Toc417979043file:///C:/Users/sony/Desktop/RAZAK_CORRECTION%20FILE%2028%20APRIL.docx%23_Toc417979045

xix

4.2 Schematic representation of hybrid DE-ESNN learning 74

4.3 Population encoding method.(Schliebs et al., 2009b) 75

4.4 The DE candidate structure in DE-ESNN framework 76

4.5 Process of hybrid DEPT-ESNN learning 78

4.6 DE candidate structure in DEPT-ESNN framework 79

4.7 Comparison of the DEPT-ESNN for ten-fold cross-validation

in terms of parameter analysis 88

4.8 Comparison of the proposed methods in terms of

convergence time for the appendicitis data set: (a) DE-

ESNN, (b) DEPT-ESNN. 91


convergence time for the Haberman data set: (a) DE-ESNN,

(b) DEPT-ESNN. 92


convergence time for the heart data set: (a) DE-ESNN, (b)

DEPT-ESNN. 94


convergence time for the hepatitis data set: (a) DE-ESNN, (b)

DEPT-ESNN. 94


convergence time for the ionosphere data set: (a) DE-ESNN,

(b) DEPT-ESNN. 96


convergence time for the iris data set: (a) DE-ESNN, (b)

DEPT-ESNN. 97


convergence time for the liver data set: (a) DE-ESNN, (b)

DEPT-ESNN. 98

5.1 Summary of the proposed MOO methods 102

5.2 Schematic representation of the proposed MODE-ESNN 103

xx

5.3 Flow chart of the hybrid HSMODE-ESNN 107

5.4 Schematic representation of the proposed MEHSMODE-

ESNN 110

5.5 Comparison of the MODE-ESNN for ten-fold cross-

validation in terms of parameter analysis 113

5.6 Comparison of the HSMODE-ESNN for ten-fold cross-


5.7 Comparison of the MEHSMODE-ESNN for ten-fold cross-


5.8 Evolution of accuracy and pre-synaptic neurons on (a)

Appendicitis, (b) Haberman, (c) Iris from the proposed

method MODE-ESNN. 122

5.9 Comparison of MOO methods with ESNN in terms of

convergence time for the appendicitis data set 124


convergence time for the Haberman data set 126


convergence time for the heart data set 126

5.12 Comparison of MOtable O methods with ESNN in terms of

convergence time for the hepatitis data set 126


convergence time for the ionosphere data set 126


convergence time for the iris data set 127


convergence time for the liver data set 129

6.1 A comparison of the proposed methods for 10-fold cross-

validation in terms of Mod parameter analysis 134

6.2 Comparison of the proposed methods for 10-fold cross-

validation in terms of Sim parameter analysis 136

125

127

128

xxi

6.3 Comparison of the proposed methods for ten-fold cross-

validation in 137


validation in terms of pre-synaptic neurons analysis 138


validation in terms of sensitivity analysis 140


validation in terms of SPE analysis 142


validation in terms of GM analysis 143


validation in terms of accuracy analysis 144


validation in terms of NPV analysis 145


validation in terms of PPV analysis 146


validation in terms of ASP analysis 147

6.12 Average training accuracy of proposed methods with data

mining methods 155

6.13 Average testing accuracy of proposed methods with data

mining methods 156

6.14 Average training accuracy of proposed methods with various

classifiers and data mining methods 157

6.15 Average testing accuracy of proposed methods with various

classifiers and data mining methods 157

6.16 Average classification's measure performance of proposed

methods with some standard classifiers for the appendicitis

data set 162

xxii

6.17 Average classification's measures performance of proposed

methods with some standard classifiers for the Haberman

data set 163


methods with some standard classifiers for the heart data set 164


methods with some standard classifiers for the hepatitis data

set 165


methods with some standard classifiers for the ionosphere

data set 166


methods with some standard classifiers for the iris data set 167


methods with some standard classifiers for the liver data set 168

6.23 Performance comparisons of the proposed and existing

methods 171

7.1 Schematic representation of the research objectives achieved 175

xxiii

LIST OF ABBREVIATIONS

ACC - Accuracy

ANNs - Artificial Neural Networks

ASP - Average Site Performance

BP - Back-Propagation

CM - Current method (ESNN)

CPSO - Cooperative Particle Swarm Optimization

DE - Differential Evolution

DE-ESNN - Differential Evolution with Evolving Spiking

Neural Network

DEPT-ESNN

- Differential Evolution for Parameter Tuning with

Evolving Spiking Neural Network

DM - Decision Making

EAs - Evolutionary Algorithms

ErrR - Error Rate

ESNN - Evolving Spiking Neural Network

FN - False Negative

FNR - False Negative Rate

FP - False Positive

FPR - False Positive Rate

GA - Genetic Algorithm

GM - Geometric Mean

GRF - Gaussian Receptive Fields

HM - Harmony Memory

HMCR - Harmony Memory Considering Rate

HMS - Harmony Memory Size

HS - Harmony Search algorithm

xxiv

HSA - Harmony Search Algorithm

HSMODE-ESNN

- Harmony Search Multi objective Differential

Evolution with Evolving Spiking Neural Network

LTD - Long Term Depression

LTP - Long Term Potentiation

MEHSMODE-ESNN

- Memetic Harmony Search Multi objective

Differential Evolution with Evolving Spiking

Neural Network

ML - Machine Learning

MLP - Multilayer Perceptron Network

Mod - Modulation Factor

MODE-ESNN

- Multi objective Differential Evolution with

Evolving Spiking Neural Network

MOEAs - Multi-objective evolutionary algorithms

MOGA - Multi objective genetic algorithm

MOO - Multi-Objective Optimization

MuSpiNN - Multi-Spiking Neural Network

NPV - Negative Predictive Value

NRU - No right to use

PAR - Pitch Adjusting Rate

PM - Proposed Method

PNNs - Probabilistic Neural Networks

PPV - Positive Predictive Value

PSO - Particle Swarm Optimization

QiPSO - Quantum-inspired Particle Swarm Optimization

raccept - Accepting Rate

SA - Simulated Annealing

SEN - Sensitivity

SI - Swarm Intelligence

Sim - Neuron Similarity Value

SNN - Spiking Neural Network

SO - Single Objective

SOM-AC - Self-Organizing Map with modified adaptive

coordinates

SPE - Specificity

SRM

- Spike Response Model

xxv

STDP - Spike Time Dependent Plasticity

SVM - Support Vector Machine

SWRNN - Spiking Wavelet Radial Basis Neural Network

Threshold - Proportion Factor

TN - True Negative

TNR - True Negative Rate

TP - True Positive

TPR - True Positive Rate

TS - Tabu Search

xxvi

LIST OF APPENDICES

APPENDIX TITLE

PAGE

A Data distribution 191

1

CHAPTER 1

INTRODUCTION

1.1 Overview

Classification of patterns is vital for several data mining processes.

Classification is one of the most commonly obverse processing tasks for a decision

support system (Ahmed et al., 2013a). There are many areas in life which need

classification such as medical diagnoses, medicine, science, industry, speech

recognition and handwritten character recognition. Among feasible classifiers,

artificial neural network (ANN) classifiers have proved to be one of the most robust

classification systems; their ability to deal with noisy input patterns and to handle

both noisy and continuous data demonstrates their use as an important tool for

classification (Mitchell and Michell, 1997).

ANNs are amongst the most well-known brain computational models and

ANN solves problems that are based on standard algorithmic techniques. ANNs can

be utilized in pattern recognition, generalization, perception and non-linear control.

Action potentials or spikes are responsible for all communications between neurons;

however, individual spikes in ANN models are averaged out over time. All

interactions are divided by the mean firing rate of the neurons. Furthermore, they are

computationally more powerful than ANNs, which use mean firing rates (Maass and

Bishop, 2001).

Due to its effectiveness in ANNs, the sigmoidal neuron model is considered

to be one of the best models of the biological neuron. Several vital applications of

ANNs have been built by rate modeling, which means a single biological neuron

2

releases action potentials (spikes) as a monotonically increasing function of input-

match. From another point of view, explorations of the computational power in

single spikes have been undertaken due to the spiking nature of biological neurons.

Bohte et al. (2002a) prove that more powerful computation can be found through

individual spike times rather than sigmoidal activation functions.

Spiking neural networks (SNNs), the third generation of ANNs, play an

essential role in biological information processing (Gerstner and Kistler, 2002).

Compared with ANNs, which use rate coding for neuronal activity representation,

spiking models provide an in-depth description of biological neuronal behavior.

More information has been used with the average firing rate for computations with

real neurons. Furthermore, instead of rate coding, the difference in firing times may

be used (Belatreche et al., 2006).

Although there are many models of SNN, the evolving spiking neural

network (ESNN) is used widely in recent research. The ESNN has several

advantages (Schliebs et al., 2009c) including being a simple, efficient neural model

and trained by a fast one-pass learning algorithm. The evolving nature of the model

can be updated whenever new data becomes accessible with no requirement to

retrain earlier existing samples. However, the ESNN model is affected by the choice

of parameter; the correct selection of parameters allows the network to evolve

towards reaching the best structure, thus guaranteeing the best output. For this

reason, an optimizer is needed to find the best combination of parameters.

Optimization has been used to enhance the ESNN algorithm. Choosing a

good optimization algorithm for real-world applications is necessary, especially for

optimal solutions of an ESNN. Evolutionary algorithms (EAs), mainly differential

evolution (DE), are common competitors in optimization problems because of the

following characteristics: simpler implementation, better performance, very few

control parameters and low space complexity (Abbass, 2001; Das and Suganthan,

2011). Therefore, DE is conducted to enhance ESNN algorithms. However, many

real-world optimization problems include several contradictory objectives. Rather

than single optimization, multi-objective optimization (MOO) can be utilized as a set

of optimal solutions to solve these problems. Every MOO solution appears to be a

3

new trade-off between the objectives. The key objective of MOO is to improve

ESNN optimal solutions of both structure and classification accuracy. In addition,

optimization of both accuracy and complexity leads to generalization.

The MOO approach is preferred to algorithms of traditional learning for a

number of reasons. First, as a result of using MOO, a good performance of these

learning algorithms can be achieved (Abbass, 2003b). Second, various objectives are

taken into consideration in the generation of multiple learning models such as

accuracy, complexity (Igel, 2005; Jin, 2006; Jin et al., 2004), interpretability and

accuracy (Jin et al., 2005), multiple error measures (Fieldsend and Singh, 2005).

Third, it is superior to build learning ensembles to use models that are produced

using MOO (Abbass, 2003a; Chandra and Yao, 2004; Jin et al., 2004). The

important goal of the MOO algorithm is to find a set of solutions from which the best

one is chosen. Based on Tan et al. (2001), the ability of EAs to search for optimal

solutions gives them the priority to be selected in MOO problems. EAs have the

ability to explore different parts of the related algorithm in the optimal set because

of the population-based algorithms.

Moreover, one of the EAs i.e. harmony search (HS) algorithm was

utilized to overcome problems of convergence rate at finding the global

minimum of DE (Gao et al., 2014; Purnomo and Wee, 2014; Wang and

Guo, 2013). Subsequently, backpropagation (BP) was used to speed up

convergence known as a memetic approach.

1.2 Problem Background

In SNN, the behavior which is archived in topology is like that of Kohonen’s

self-organization map and can be used effectively in character recognition

(Buonomano and Merzenich, 1999), classifications and dynamic path planning

(Yang and Luo, 2004). Consequently, SNNs have been utilized as a vital method for

classification. Many types of SNN are used for classification problems. Bohte et al.

(2002a) proposed a supervised learning algorithm, spike backpropagation

4

(SpikeProp), using spike-time encoding based on error BP, which is used for solving

classification problems. Schrauwen et al. (2004) proposed many learning rules to

extend SpikeProp for good learning of spike times. Ghosh-Dastidar et al. (2009)

introduced a multi-SpikeProp for supervised learning of spike patterns in multiple-

synapse transmission (Bohte et al., 2002b). Ahmed et al. (2013a) proposed and

presented several methods for classification problems for an improved SpikeProp by

particle swarm optimization (PSO) and angle-driven dependency learning rate.

Ahmed et al. (2014) mentioned that the most important challenge is to find out

efficient learning rules that might take advantage of the specific features of SNNs

while keeping the advantageous properties (general-purpose, easy-to-use, available

simulators etc.) of traditional connectionist models.

There have been many attempts to improve new models of SNNs. Wysoski et

al. (2006c) proposed a new model type, ESNN. Recently, a few studies on the

hybridization of the ESNN algorithm have been implemented. A novel supervised

learning algorithm combined with PSO for this model ESNN has been introduced by

Hamed et al. (2011a).

The most significant problem facing these recent studies is to determine the

optimal number of pre-synaptic neurons for a given data set (Hamed et al., 2011a).

The number of pre-synaptic neurons is required before the ESNN structure can be

constructed. This problem is similar to identifying the number of hidden nodes in

multilayer perceptron (MLP). Based on the work by Hamed (2012), a smaller

number of pre-synaptic neurons cause fewer input spikes to be generated and may

subsequently affect learning accuracy, while a larger number increases

computational time. Evolving processes are difficult to model as there might be no

prior knowledge for some parameters (Kasabov, 2003). Figure 1.1 explains the

scenario which leads to the problem settled by this research. In Figure 1.1 the

challenges of ESNN model and the limitation of existing model are revealed.

5

Evolving spiking neural network (ESNN)

1. ESNN cannot identify number of pre-synaptic neurons which is required before the ESNN

structure

2. ESNN cannot identify the optimum values of parameters

3. Difficulty in achieving optimization trade-off between accuracy and ESNN structure.

What do we need? A sufficient solution that guarantees the optimum number

of pre-synaptic neurons, optimum values of ESNN parameters and high performance of classification

accuracy.

Limitations of existing ESNN

model 1. Predetermined number of pre-

synaptic neurons

2. Predetermined values of ESNN parameters.

3. Not ensured high performance of classification accuracy.

Challenges of ESNN model 1. Fixed number of pre-synaptic

neurons affects the ESNN

performance

2. ESNN performance is influenced by fixed values of its parameters.

3. Single optimization has an impact on performance of classification

accuracy.

Required solution should guarantee 1. High accuracy performance. 2. Improved ESNN structure (optimum pre-

synaptic neurons).

Figure 1.1 Scenario guides to the research problem

The scenario shown in Figure 1.1 draws attention to the most important

requirements that should be taken into consideration during the design of the

solution.

6

According to Watts (2009), the automatic selection of evolving connectionist

systems (ECOS) training parameters would be a significant advantage. Therefore, it

would be interesting to choose an optimization technique to carry out this parameter

adaptation. Among the various optimization techniques, EAs have been used to solve

learning problems when applied to the ESNN model. The significance of using EAs

is their ability to adapt to a varying environment (Fernandez Caballero et al., 2010);

that is why it is a common optimizer in many classification models such as ANNs

(da Silva et al., 2010; Mineu et al., 2010), wavelet neural networks (Dheeba and

Selvi, 2012) and support vector machine (SVM) (Zhou et al., 2007). To capitalize on

the particular advantage of DE, it can be an attractive method to optimize pre-

synaptic neurons and to find trade-off solutions to overcome the problems of MOO.

A few studies have evaluated multi-objective evolutionary algorithm

(MOEAs) methods with SNN such as multi-objective genetic algorithm (MOGA)

with SpikeProp and showed that this algorithm performs well (Jin et al., 2007a). As

Yee and Teo suggested in (2013) multi-objective techniques could generate better

solutions in SNNs. Therefore, this research improved new hybrid methods with other

types of SNNs, for example ESNN with MOEAs such as multi-objective differential

evolution with evolving spiking neural network (MODE-ESNN), harmony search

multi-objective differential evolution with evolving spiking neural network

(HSMODE-ESNN) and memetic harmony search multi-objective differential

evolution with evolving spiking neural network (MEHSMODE-ESNN). The

proposed methods aim to create a trade-off between the structures of ESNN and the

accuracy of testing data of ESNN.

1.3 Problem Statement

Issue 1: Recently, ESNNs have attracted extensive research attention

because of the multiple advantages they offer compared to others models (Batllori et

al., 2011; Kasabov, 2012; Kasabov et al., 2014; Mohemmed et al., 2013; Murli et

al., 2014; Nuntalid et al., 2011a; Schliebs and Kasabov, 2013). Among the many real

issues that need to be explored in ESNN, determining the optimal number of pre-

7

synaptic neurons for a given data set is the most important one (Hamed, 2012;

Kasabov et al., 2014). The number of pre-synaptic neurons is required before the

ESNN structure can be constructed. This problem is similar to identifying the

number of hidden nodes in MLP. Fewer pre-synaptic neurons cause the generation of

fewer input spikes, which may subsequently affect learning accuracy, while more

pre-synaptic neurons increase computational time. Additionally, each of the methods

has a number of parameters which are currently set by hand, based on performance

with the training data set. Therefore, the automation of the process of parameter

selection is another challenge (Kasabov, 2012; Kita, 2011; Pears et al., 2013; Yu et

al., 2014).

Issue 2: Another real issue of the ESNN is achieving an optimized balance

between accuracy and the network structure. Several integrations between EAs and

Swarm Intelligence (SI) strategies with ESNN have been performed such as: (Hamed

et al., 2009a; Schliebs et al., 2009b; Schliebs et al., 2010a). However, GA has some

shortcomings such as more predefined parameters, competing conventions and

premature convergence problem (Kim et al., 2005; Sahab et al., 2005). Nevertheless,

no specific algorithm can achieve the best performance for particular problems as

supposed to the 'no free lunch theorem' (Wolpert and Macready, 1997). On the other

hand, the many advantages of DE compared to PSO and GA, which include being

much simpler to implement, much better performance, very few control parameters

and low space complexity (Abbass, 2001; Das and Suganthan, 2011) motivate

research in utilizing this hybridization

Therefore, in this thesis, all the hybrid proposed methods: differential

evolution with evolving spiking neural network (DE-ESNN), differential evolution

for parameter tuning with evolving spiking neural network (DEPT-ESNN), multi

objective differential evolution with evolving spiking neural network (MODE-

ESNN), harmony search multi objective differential evolution with evolving spiking

neural network (HSMODE-ESNN) and memetic harmony search multi objective

differential evolution with evolving spiking neural network (MEHSMODE-ESNN)

are presented.

8

Based on the above issues which are mentioned in section 1.3, the main

research question is

Are the proposed hybrid methods between ESNN and different meta-heuristic

and MOEAs which include DE-ESNN, DEPT-ESNN, MODE-ESNN, HSMODE-

ESNN and MEHSMODE-ESNN, beneficial for evolving learning of ESNN in terms

of structure (pre-synaptic neurons) and accuracy?

Thus, the following issues need to be addressed:

1. How to optimize both the structure of ESNN (the pre-synaptic neurons )

using the proposed method (DE-ESNN) and ESNN parameters using the

proposed method (DEPT-ESNN) ?

2. How to improve a multi objective method to optimize ESNN’s pre-

synaptic neurons as well as the parameters simultaneously using MODE-

ESNN ?

3. How effective is harmony search (HS) and memetic technique in

enhancing the multi objective method (MODE-ESNN) using HSMODE-

ESNN and MEHSMODE-ESNN?

4. Would the classification accuracy and other classification performance

measures be improved when all the previous proposed methods are

implemented ?

1.4 Research Aim

This research aims to enhance hybrid learning of evolving spiking neural

network (ESNN) with the proposed methods to obtain simple (the lowest number of

pre-synaptic neurons) and accurate ESNN model.

9

1.5 Research Objectives

In order to find the answers to the above questions, the objectives of this

study have been identified as:

1. To enhance evolving spiking neural network (ESNN) learning by

proposing hybrid methods using a differential evolution (DE)

algorithm to optimize the pre-synaptic neurons and the parameters of

ESNN.

2. To improve a multi-objective hybrid method of multi objective

differential evolution with evolving spiking neural network(MODE-

ESNN) to optimize the pre-synaptic neuron as well as the parameters

simultaneously.

3. To enhance the proposed hybrid methods using HS and memetic

techniques.

1.6 Research Scope

To accomplish the above objectives, the scope of this study is restricted to the

following:

1. Data sets on both binary and multi class classification problems are

essential for evaluating the proposed methods Appendicitis, Iris,

Hepatitis, Ionosphere, Liver, Haberman and Pima heart.

2. Focus is on the proposed methods of DE-ESNN, DEPT-ESNN, MODE-

ESNN, HSMODE-ESNN and MEHSMODE-ESNN for learning, which

includes training and testing in classification problems.

3. Performance is tested based on structure (number of pre-synaptic

neurons), classification accuracy (ACC), geometric mean (GM),

sensitivity (SEN), specificity (SPE), positive predictive value (PPV),

negative predictive value (NPV) and average site performance (ASP).

4. The programs are customized, improved and applied to the learning of

ESNN using Microsoft Visual C++ 10.0 and Matlab.

10

1.7 Research Methodology Overview

This study consists of five phases: research planning, data set preparation,

research design, implementation and analysis. Research planning is the key to

success in guiding the research direction. Additionally, algorithm performance

depends on data set nature. The data sets were used as inputs the for the proposed

methods in research design and implementation of the process enhancement. The

proposed algorithms were trained, tested and validated using quantitative

measurements to classification problems. Finally, statistical test analysis was

applied. Figure 1.2 shows the research methodology of the study.

11

Figure 1.2 Flow of research methodology phases

Phase 2: Data-set preparation

Data collection

Data preprocessing

Phase 3: Research Design

DE-ESNN

DEPT-ESNN

MODE-ESNN

HSMODE-ESNN

MEHSMODE-ESNN

Phase 5: Quantitative analysis

Classification analysis

Statistical test

HSMODE-

ESNN and

MEHSMODE-

ESNN

Phase 4: Research implementation

Parameter setting

Validation

Training

Testing

Phase 1: Research Planning

Milestone

Gantt Chart

HSMODE-

ESNN and

MEHSMODE-

ESNN

12

1.8 Summary of Research Contributions

The contributions of the study can be summarized in the next points, as also

illustrated in Figure 1.3:

1. Hybrid method known as DE-ESNN used for optimizing the pre-synaptic

neurons.

2. Hybrid method known as DEPT-ESNN used to optimize the parameters

(Mod, Sim, Threshold) in ESNN.

3. Multi-objective method known as MODE-ESNN used to optimize the pre-

synaptic. neurons and the parameters simultaneously.

4. Hybrid methods known as HSMODE-ESNN used to enhance the MODE-

ESNN method.

5. Hybrid methods known as MEHSMODE-ESNN used to enhance the

MODE-ESNN method.

Figure 1.3 Summary of research contributions

Hybrid method known as DE-ESNN

Hybrid method known as DEPT-ESNN

Multi objective method known as MODE-ESNN

Hybrid methods known as HSMODE-ESNN

Hybrid methods known as MEHSMODE-ESNN

13

1.9 Thesis Outline

This thesis contains seven chapters, including the introduction chapter. The

second chapter describes the background and the earlier work in the field of SNNs,

evolving spiking neural network and MOEAs. The third chapter describes the

research methodology for the work. The fourth and fifth chapters present the

proposed methods used in this study and their algorithmic and results details. Finally,

the last two chapters present the performance evaluation, discussion, conclusion and

future extensions of the study.

Chapter 2, Literature Review, introduces a general overview of the

literature review of this study. Fundamental concepts of SNNs, ESNN and EAs

that are used in this thesis and MO optimization are introduced.

Chapter 3, Research Methodology, illustrates the methodology used

in this study. The research methodology is presented as a flow chart diagram that

explains briefly how each step is utilized.

Chapter 4, Hybrid Proposed Methods, explains in detail how EAs can

optimize the ESNN model for classification. Furthermore, this chapter describes

the implementation of the algorithms which are used. Moreover, the results based

on performance measures are illustrated for all proposed methods. Last but not

least, statistical analysis is carried out.

Chapter 5, MOO Proposed Methods, describes how multi-objective

algorithms can optimize the ESNN model for classification. Additionally, this

chapter explains the implementation of MOO algorithms that are used.

Moreover, the results based on performance measures are illustrated for all

proposed methods. Finally, statistical analysis is carried out.

Chapter 6, Comparative study of the proposed methods, implements

the results based on performance measures and illustrates the comparative

analysis among all proposed methods. Moreover, a comparison is conducted with

various classifiers and data mining algorithms. Last but not least, statistical

analysis is carried out.

14

Chapter 7, Conclusion and Future Work, winds up the study and

highlights the contributions and findings of the research work. In addition,

Chapter 7 provides recommendations and suggestions and for future work.

Finally , a summary is reported for the whole study.

179

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