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CHAPTER 16
Neural Computing Applications,
and Advanced Artificial IntelligentSystems and Applications
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Neural Computing Applications,and Advanced Artificial Intelligent
Systems and ApplicationsSeveral Real-World Applications of ANN TechnologyAdvanced AI Systems
Genetic Algorithms Fuzzy Logic Qualitative Reasoning
Integration (Hybrids)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Areas of ANN Applications:
An OverviewRepresentative Business ANN Applications
AccountingFinance
Human ResourcesManagementMarketing
Operations
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Accounting
Identify tax fraudEnhance auditing by finding irregularities
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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FinanceSignatures and bank note verificationsMortgage underwritingForeign exchange rate forecastingCountry risk rating
Bankruptcy predictionCustomer credit scoringCredit card approval and fraud detection
Stock and commodity selection and trading
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Finance 2
Credit card profitabilityForecasting economic turning pointsBond rating and trading
Pricing initial public offeringsLoan approvalsEconomic and financial forecastingRisk management
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Human Resources
Predicting employees performance and behaviorDetermining personnel resource requirements
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Management
Corporate merger predictionCountry risk rating
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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MarketingConsumer spending pattern classification
Customers characteristicsSales forecastsData miningAirline fare managementDirect mail optimizationTargeted marketing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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OperationsAirline crew schedulingPredicting airline seat demandVehicle routingAssembly and packaged goods inspectionQuality controlMatching jobs to candidatesProduction/job scheduling
Factory process control
Many More
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Credit Approval
with Neural Networks
Increases loan processor productivity by 25 to35% over other computerized tools
Also detects credit card fraud
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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The ANN MethodData from the application and into a database
Preprocess applications manually
Neural network trained in advance with manygood and bad risk cases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Neural Network Credit Authorizer
Construction ProcessStep 1: Collect data
Step 2: Separate data into training and test sets
Step 3: Transform data into network inputs
Step 4: Select, train, and test network
Step 5: Deploy developed network application
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Bankruptcy Prediction
with Neural NetworksConcept Phase
Paradigm: Three-layer network, back-propagation
Training data: Small set of well-known financial ratios
Data available on bankruptcy outcomes
Supervised network
Training time not to be a problemDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Application Design
Five Input NodesX1: Working capital/total assets
X2: Retained earnings/total assetsX3: Earnings before interest and taxes/total assetsX4: Market value of equity/total debt
X5: Sales/total assets
Single Output Node: Final classification for each firm Bankruptcy or Nonbankruptcy
Development Tool: NeuroShellDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Development Three-layer network with backpropagation (Figure 16.3) Continuous valued input Single output node: 0 = bankrupt, 1 = not bankrupt
Training Data Set: 129 firms Training Set: 74 firms; 38 bankrupt, 36 not Ratios computed and stored in input files for:
The neural network A conventional discriminant analysis program
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Architecture of the Bankruptcy Prediction
Neural Network(Figure 16.3)
X1
Bankrupt 0
Not bankrupt 1
X2
X3
X4
X5
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Parameters Learning threshold Learning rate Momentum
Testing Two Ways
Test data set: 27 bankrupt firms, 28 nonbankrupt firms Comparison with discriminant analysis
The neural network correctly predicted:
81.5 percent bankrupt cases 82.1 percent nonbankrupt cases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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ANN did better predicting 22 out of the 27 actual cases
Discriminant analysis predicted only 16 correctly
Error Analysis Five bankrupt firms misclassified by both methods Similar for nonbankrupt firms
Neural network at least as good as conventional
Accuracy of about 80 percent is usually acceptable forneural network applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Stock Market Prediction System with
Modular Neural Networks
Accurate Stock Market Prediction - Complex Problem
Several Mathematical Models - Disappointing Results
Fujitsu and Nikko Securities: TOPIX Buying and Selling
Prediction System
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Input: Several technical and economic indexes
Several modular neural networks relate past
indexes, and buy/sell timing
Prediction system Modular neural networks Very accurate
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Network Architecture
(Figure 16.4)
Network Model: 3 layers, standard sigmoid function,
continuous output [0, 1]
High-speed Supplementary Learning Algorithm
Training Data Data Selection Training Data
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Preprocessing: Input Indexes - Converted into spatialpatterns, preprocessed to regularize them
Moving Simulation Prediction Method (Figure 16.5)
Result of Simulations Simulation for Buying and Selling Stocks Example (Figure 16.6) Excellent Profit
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Integrated ANNs and
Expert Systems1. Resource Requirements Advisor
Advises users on database systems resource requirements
Predicts the time and effort to finish a database project
ES shell AUBREY and neural network tool NeuroShell
ES supported data collection
ANN used for data evaluation
ES final analysis
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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2. Personnel Resource Requirements Advisor
Project personnel resource requirements for maintainingnetworks or workstations at NASA
Rule-based ES determines the final resource projections
ANN provides project completion times for services requested(Figure 16.7)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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3. Diagnostic System for an Airline
Singapore Airlines
Assists technicians in diagnosing avionics equipment
INSIDE (Inertial Navigation System Interactive Diagnostic Expert)
Designed to reduce the diagnostic time(Figure 16.8)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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4. Manufacturing Product Liability
United Technologies Carrier Two ES + ANN Patterns fed into multilayer feedforward ANN Integrated with a database into an Automatic Early Warning
System (AEWS)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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5. Oil Refinery Production Scheduling andEnvironmental Control
Citgo Petroleum Corporation Lower costs Improved safety Higher product quality Higher yields
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Genetic Algorithms
Goal (evolutionary algorithms): Demonstrate self-organization and adaptation by exposure to theenvironmentSystem learns to adapt to changes .Example 1: Vector Game
Random trial and error Genetic algorithm solution
Process (Figure 16.9)Example: the game of MasterMind
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Genetic Algorithm
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Genetic Algorithm
Definition and ProcessGenetic algorithm : "an iterative procedure maintaining a
population of structures that are candidate solutions to
specific domain challenges (Grefenstette [1982])
Each candidate solution is called a chromosome
Chromosomes can copy themselves, mate, and mutate
Use specific genetic operators - reproduction, crossoverand mutation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
f
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Primary Operators of
Most Genetic Algorithms
Reproduction
Crossover
Mutation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Genetic Algorithm Operators
1 0 1 0 1 1 1
1 1 0 0 0 1 1
Parent 1
Parent 2
1 0 1 0 0 1 1
1 1 0 0 1 1 0 Mutation
Child 1
Child 2
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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GA Example: The Knapsack Problem
Item: 1 2 3 4 5 6 7Benefit: 5 8 3 2 7 9 4Weight: 7 8 4 10 4 6 4Knapsack holds a maximum of 22 poundsFill it to get the maximum benefitSolutions take the form of a string of 1s
Solution: 1 1 0 0 1 0 0Means choose items 1, 2, 5. Weight = 21, Benefit = 20Evolver solution in Figure 16.10
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Genetic Algorithms
Applications and Software
Type of machine learning
Set of efficient, domain-independent searchheuristics for a broad spectrum of applications
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Genetic Algorithm Application Areas
Dynamic process controlInduction of rule optimization
Discovering new connectivity topologiesSimulating biological models of behavior and evolutionComplex design of engineering structuresPattern recognitionSchedulingTransportationLayout and circuit designTelecommunicationGraph-based problems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Business Applications
Channel 4 Television (England) to schedule commercialsDriver scheduling in a public transportation systemJobshop schedulingAssignment of destinations to sourcesTrading stocksProductivity in whisky-making is increased
Often genetic algorithm hybrids with other AI methods
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Representative Commercial Packages
Evolver (Excel spreadsheet add-in)Genetic Algorithm User Interface (GAUI)OOGA (Object-Oriented GA for industrial use)
XperRule Genasys (ES shell with an embedded geneticalgorithm)Sugal Genetic Algorithm Simulator
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Optimization Algorithms
Via neural computing sometimes
Genetic algorithms and their derivatives can optimize(or nearly optimize) complex problems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Fuzzy Logic
Fuzzy logic deals with uncertainty
Uses the mathematical theory of fuzzy sets
Simulates the process of normal human reasoning
Allows the computer to behave less precisely and
logically
Decision making involves gray areas and the term maybe
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
d
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Fuzzy Logic Advantages
Provides flexibilityProvides options
Frees the imaginationMore forgivingAllows for observationShortens system development timeIncreases the system's maintainabilityUses less expensive hardwareHandles control or decision-making problems noteasily defined by mathematical models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Fuzzy Logic Example:
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y g pWhat is Tall?
In-Class ExerciseProportion
Height Voted for510 0.05511 0.10
6 0.6061 0.1562 0.10
Jack is 6 feet tall Probability theory - cumulative probability There is a 75 percent chance that Jack is tall
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Fuzzy logic Jack's degree of membership within the
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Fuzzy logic - Jack s degree of membership within theset of tall people is 0.75We are not completely sure whether he is tall or not
Fuzzy logic - We agree that Jack is more or less tall
Membership Function< Jack, 0.75 Tall >
Knowledge-based system approach: Jack is tall(CF = .75)Belief functions
Can use fuzzy logic in rule-based systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Membership Functions in Fuzzy Sets
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Membership Functions in Fuzzy Sets(Figure 16.11)
Short Medium Tall
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Membership
Height in inches (1 inch = 2.54 cm)
0.5
1.0
64 7469
Fuzzy Logic Applications and
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y g pp
SoftwareDifficult to apply when people provide evidence
Used in consumer products that have sensors Air conditioners Cameras Dishwashers Microwaves Toasters
Special software packages
Controls applicationsDecision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
E l f F L i
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Examples of Fuzzy Logic
Example 1: Strategic planning STRATASSIST - fuzzy expert system that helps small- to
medium-sized firms plan strategically for a single product
Example 2: Fuzziness in real estate
Example 3: A fuzzy bond evaluation system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Fuzzy Logic Software
Fuzzy Inference Development Environment(FIDE)Z Search
HyperLogic Corporation demosOthers
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Q lit ti R i g (QR)
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Qualitative Reasoning (QR)
Means of representing and making inferences usinggeneral, physical knowledge about the world
QR is a model-based procedure that consequentlyincorporates deep knowledge about a problemdomain
Typical QR Logic If you touch a kettle full of boiling water on a stove, you
will burn yourself If you throw an object off a building, it will go down
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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But
No specific knowledge about boiling temperature, justthat it is really hot!
No specific information about the building or object,unless you are the object, or you are trying to catch it
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Main goal of QR: To represent common senseknowledge about the physical world, and the underlyingabstractions used in quantitative models (objects fall)
Given such knowledge and appropriate reasoningmethods, an ES could make predictions and diagnoses,and explain the behavior of physical systems
qualitatively, even when exact quantitative descriptionsare unavailable or intractable
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Qualitative Reasoning
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Q g
Relevant behavior is modeled
Temporal and spatial qualities in decision making arerepresented effectively
Applies common sense mathematical rules to variablesand functions
There are structure rules and behavior rules
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Some Real-World QR Applications
Nuclear plant fault diagnoses
Business processes
Financial markets
Economic systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Intelligent S stems Integr tion
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Intelligent Systems Integration
Combine Neural Computing
Expert Systems Genetic Algorithms Fuzzy Logic
Example: International investment management--stock selectionFuzzy Logic and ANN (FuzzyNet) to forecast theexpected returns from stocks, cash, bonds, and otherassets to determine the optimal allocation of assets
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Global marketsIntegrated network architecture of the system
(Figure 16.12)
TechnologiesExpert system (rule-based) for country and stock selectionNeural network for forecastingFuzzy logic for assessing factors without reliable data
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
FuzzyNet Architecture
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FuzzyNet Architecture(Figure 16.12)
Membership Function Generator (MFG)
Fuzzy Information Processor (FIP)
Back-propagation Neural Network (BPN)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Data Mining and Knowledge
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Data Mining and Knowledge
Discovery in Databases (KDD)Hidden value in data
Knowledge Discovery in Databases (KDD)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
The KDD Process
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The KDD Process
Start with Raw Data and Do
1. Selection to produce target the appropriate datawhich undergoes
2. Preprocessing to filter the data in preparation for
3. Transformation so that4. Data Mining can identify patterns that go through5. Interpretation and Evaluation resulting in
knowledge
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
D Mi i
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Data Mining
Find kernels of value in raw data ore
Theoretical advances
Knowledge discovery in textual databases
Methods based on statistics, cluster analysis, discriminant
analysis, fuzzy logic, genetic algorithms, and neural networks
Ideal for data mining
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
AI Methods and Data Mining
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g
for SearchNeural Networks
Expert Systems
Rule Induction
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
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Data Mining Applications Areas
Marketing
Investment
Fraud detection
Manufacturing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Information Overload
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Information Overload
Data mining methods can sift through softinformation to identify relationships automatically
Intelligent agents
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Important KDD andData Mining Challenges
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Data Mining Challenges
Dealing with larger databases
Working with higher dimensionalities of data
Overfitting--modeling noise rather than data patterns
Assessing statistical significance of results
Working with constantly changing data and knowledge
Continue
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ
Working thro gh missing and nois data
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Working through missing and noisy data
Determining complex relationships between fields
Making patterns more understandable to humans
Providing better user interaction and prior knowledgeabout the data
Providing integration with other systems
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson6th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ