7 Classification

8/2/2019 7 Classification

1/63

1

Data Mining:Concepts and Techniques

Classification and Prediction


2/63

2

What is classification?

What is prediction?


3/63

3

Classification:

predicts categorical class labels

classifies data (constructs a model) based on thetraining set and the values (class labels) in a

classifying attribute and uses it in classifying new data Prediction:

models continuous-valued functions, i.e., predictsunknown or missing values

Typical Applications credit approval

target marketing

medical diagnosis

treatment effectiveness analysis

Classification vs. Prediction


4/63

4

ClassificationA Two-Step Process

Model construction: describing a set of predetermined classes

Each tuple/sample is assumed to belong to a predefined class,as determined by the class label attribute

The set of tuples used for model construction: training set

The model is represented as classification rules, decision trees,or mathematical formulae

Model usage: for classifying future or unknown objects

Estimate accuracy of the model

The known label of test sample is compared with the

classified result from the model Accuracy rate is the percentage of test set samples that are

correctly classified by the model

Test set is independent of training set, otherwise over-fittingwill occur


5/63

5

Classification Process (1): ModelConstruction

Training

Data

N A M E R A N K Y E A R S T E N U R E D

Mike Assistant Prof 3 no

Mary Assistant Prof 7 yesBill Professor 2 yes

Jim Associate Prof 7 yes

Dave Assistant Prof 6 no

Anne Associate Prof 3 no

Classification

Algorithms

IF rank = professor

OR years > 6

THEN tenured = yes

Classifier

(Model)


6/63

6

Classification Process (2): Use theModel in Prediction

Classifier

Testing

Data

N A M E R A N K Y E A R S T E N U R E D

Tom Assistant Prof 2 no

Merlisa Associate Prof 7 no

George Professor 5 yes

Joseph Assistant Prof 7 yes

Unseen Data

(Jeff, Professor, 4)

Tenured?


7/637

Supervised vs. UnsupervisedLearning

Supervised learning (classification)

Supervision: The training data (observations,

measurements, etc.) are accompanied by labels

indicating the class of the observations

New data is classified based on the training set

Unsupervised learning (clustering)

The class labels of training data is unknown Given a set of measurements, observations, etc. with

the aim of establishing the existence of classes or

clusters in the data


8/638

Issues regarding classification and prediction


9/639

Issues regarding classification andprediction (1): Data Preparation

Data cleaning

Preprocess data in order to reduce noise and handle

missing values

Relevance analysis (feature selection)

Remove the irrelevant or redundant attributes

Data transformation

Generalize and/or normalize data


10/6310

Issues regarding classification and prediction(2): Evaluating Classification Methods

Predictive accuracy (correctly predict the class of newdata)

Speed and scalability time to construct the model

time to use the model Robustness

handling noise and missing values Scalability

efficiency in disk-resident databases Interpretability:

understanding and insight provided by the model Goodness of rules

decision tree size compactness of classification rules


11/6311

Classification by decision tree induction


12/6312

Classification by Decision TreeInduction

Decision tree

A flow-chart-like tree structure

Internal node denotes a test on an attribute

Branch represents an outcome of the test

Leaf nodes represent class labels or class distribution Decision tree generation consists of two phases

Tree construction

At start, all the training examples are at the root

Partition examples recursively based on selected attributes

Tree pruning

Identify and remove branches that reflect noise or outliers

Use of decision tree: Classifying an unknown sample

Test the attribute values of the sample against the decision tree


13/6313

Training Dataset

age income student credit_rating

40 low yes fair

>40 low yes excellent

3140 low yes excellent


14/6314

Output: A Decision Tree for buys_computer

age?

overcast

student? credit rating?

no yes fairexcellent

40

no noyes yes

yes

30..40


15/6315

Algorithm for Decision Tree Induction

Basic algorithm (a greedy algorithm)

Tree is constructed in a top-down recursive divide-and-conquermanner

At start, all the training examples are at the root

Attributes are categorical (if continuous-valued, they arediscretized in advance)

Examples are partitioned recursively based on selected attributes

Test attributes are selected on the basis of a heuristic orstatistical measure (e.g., information gain)

Conditions for stopping partitioning

All samples for a given node belong to the same class

There are no remaining attributes for further partitioningmajority voting is employed for classifying the leaf

There are no samples left


16/6316

Attribute Selection Measure

Information gain

All attributes are assumed to be categorical Can be modified for continuous-valued attributes


17/6317

Information Gain

Select the attribute with the highest information gain

Assume there are two classes, P and N

Let the set of examples Scontain pelements of class P

and nelements of class N

The amount of information, needed to decide if an

arbitrary example in Sbelongs to P or Nis defined as

np

n

np

n

np

p

np

pnpI

22 loglog),(


18/6318

Information Gain in DecisionTree Induction

Assume that using attribute A a set Swill be partitioned

into sets {S1, S2, , Sv}

IfSicontains piexamples ofPand niexamples ofN,

the entropy, or the expected information needed toclassify objects in all subtrees Si is

The encoding information that would be gained by

branching onA

1

),()(

i

iiii npI

np

npAE

)(),()( AEnpIAGain


19/6319

Attribute Selection by InformationGain Computation

Class P: buys_computer =

yes

Class N: buys_computer =

no

I(p, n) = I(9, 5) =0.940

Compute the entropy for age:

Hence

Similarly

age pi ni I(pi, ni)40 3 2 0.971

69.0)2,3(14

5

)0,4(14

4)3,2(

14

5)(

I

IIageE

048.0)_(

151.0)(

029.0)(

ratingcreditGain

studentGain

incomeGain

)(),()( ageEnpIageGain


20/6320

Extracting Classification Rules from Trees

Represent the knowledge in the form ofIF-THEN rules

One rule is created for each path from the root to a leaf

Each attribute-value pair along a path forms a conjunction

The leaf node holds the class prediction

Rules are easier for humans to understand

Example

IF age= 40 AND credit_rating= fair THEN buys_computer=no


21/6321

Avoid Overfitting in Classification

The generated tree may overfit the training data

Too many branches, some may reflect anomaliesdue to noise or outliers

Result is in poor accuracy for unseen samples

Two approaches to avoid overfitting Prepruning: Halt tree construction earlydo not split

a node if this would result in the goodness measurefalling below a threshold

Difficult to choose an appropriate threshold Postpruning: Remove branches from a fully grown

treeget a sequence of progressively pruned trees

Use a set of data different from the training datato decide which is the best pruned tree


22/6322

Approaches to Determine the FinalTree Size

Separate training (2/3) and testing (1/3) sets

Use cross validation, e.g., 10-fold cross validation

Use all the data for training but apply a statistical test (e.g., chi-square) to

estimate whether expanding or pruning a

node may improve the entire distribution

Use minimum description length (MDL) principle:

halting growth of the tree when the encoding

is minimized


23/63

23

Enhancements to basic decisiontree induction

Allow for continuous-valued attributes

Dynamically define new discrete-valued attributes thatpartition the continuous attribute value into a discreteset of intervals

Handle missing attribute values

Assign the most common value of the attribute

Assign probability to each of the possible values

Attribute construction

Create new attributes based on existing ones that aresparsely represented

This reduces fragmentation, repetition, and replication


24/63

24

Classification in Large Databases

Classificationa classical problem extensively studied by

statisticians and machine learning researchers

Scalability: Classifying data sets with millions of examples

and hundreds of attributes with reasonable speed Why decision tree induction in data mining?

relatively faster learning speed (than other classificationmethods)

convertible to simple and easy to understandclassification rules

can use SQL queries for accessing databases

comparable classification accuracy with other methods


25/63

25

Scalable Decision Tree InductionMethods in Data Mining Studies

SLIQ

builds an index for each attribute and only class list andthe current attribute list reside in memory

SPRINT

constructs an attribute list data structure

PUBLIC

integrates tree splitting and tree pruning: stop growing

the tree earlier RainForest

separates the scalability aspects from the criteria thatdetermine the quality of the tree

builds an AVC-list (attribute, value, class label)


26/63

26

Data Cube-Based Decision-TreeInduction

Integration of generalization with decision-tree induction

Classification at primitive concept levels

E.g., precise temperature, humidity, outlook, etc.

Low-level concepts, scattered classes, bushyclassification-trees

Semantic interpretation problems.

Cube-based multi-level classification Relevance analysis at multi-levels.

Information-gain analysis with dimension + level.


27/63

27

Presentation of Classification Results


28/63

28

Bayesian Classification


29/63

29

Bayesian Classification: Why?

Probabilistic learning: Calculate explicit probabilities forhypothesis, among the most practical approaches to certaintypes of learning problems

Incremental: Each training example can incrementally

increase/decrease the probability that a hypothesis iscorrect. Prior knowledge can be combined with observeddata.

Probabilistic prediction: Predict multiple hypotheses,

weighted by their probabilities Standard: Even when Bayesian methods are

computationally intractable, they can provide a standard ofoptimal decision making against which other methods can

be measured


30/63

30

Bayesian Theorem

Given training data D, posteriori probability of ahypothesis h, P(h|D)follows the Bayes theorem

Practical difficulty: require initial knowledge of manyprobabilities, significant computational cost

)(

)()|()|(

DP

hPhDPDhP


31/63

31

Naive Bayesian Classifier (simple Bayesianclassifier)works as

Each data sample is represented by an n-dimensionalfeature vector, X=(x1, x2,xn), depicting nmeasurements on the sample from n attributes,

respectively, A1,A2,,An. Suppose there are m classes, C1,C2,Cm. X is data

sample (ie having no class label), the classifier willpredict that X belongs to the class having the highest

posterior probability, conditioned on X. ie the naveBayesian classifier assigns an unknown sample X toclass Ci iff

P(Ci/X) = P(X/Ci)P(Ci) / P(X)


32/63

32

Naive Bayesian Classifier works as

P(X) is constant for all classes, only P(X/Ci)P(Ci) need to bemaximized. Class prior probabilities may be estimated by P(Ci)=si/s , where si is the no. of training samples of class Ci, and s is the totalno. of training samples.

If we have data sets with many attributes, it would be

computationally expensive to compute P(X/Ci). To reducecomputation in evaluating P(X/Ci), the nave assumption of classconditional independence is made. This presumes that the valuesof the attributes are conditionally independent of one another,given the class label of the sample ie there are no dependence

relationships among the attributes. Thus,n

P(X/Ci)=p P(xk/ Ci)

k=1


33/63

33


the probabilities P(x1/Ci), P(x2/Ci), , P(xn/Ci) can be estimatedfrom training samples, where

- If Ak is categorical, then P(xk/Ci)= sik/si,

where sikis the no. of training samples of class Ci having the

value xk for Akand si is the no. of training samples belonging toCi.

- If Ak is continuous- valued, then the attribute is assumed tohave a Gaussian distribution so that

P(xk/C

i) = g(x

k,m

Ci,s

Ci) =

where g(xk,mCi ,s Ci) is the Gaussian (normal) density functionfor attribute Ak, while mCi and s Ci are the mean and standarddeviation respectively, given the values for attribute for Aktraining samples of class Ci.


34/63

34


In order to classify an unknown sample X, P(X/Ci)P(Ci)is evaluated for each class Ci. Sample X is thenassigned to the class Ci iff

P(X/Ci)P(Ci) > P(X/Cj)P(Cj)

for 1


35/63

35

Bayesian classification

The classification problem may be formalizedusing a-posteriori probabilities:

P(C|X) = prob. that the sample tuple

X= is of class C.

E.g. P(class=N | outlook=sunny,windy=true,)

Idea: assign to sampleXthe class labelCsuchthatP(C|X) is maximal


36/63

36

Estimating a-posteriori probabilities

Bayes theorem:

P(C|X) = P(X|C)P(C) / P(X)

P(X) is constant for all classes

P(C) = relative freq of class C samples

C such that P(C|X) is maximum =C such that P(X|C)P(C) is maximum

Problem: computing P(X|C) is unfeasible!


37/63

37

The independence hypothesis

makes computation possible

yields optimal classifiers when satisfied

but is seldom satisfied in practice, as attributes

(variables) are often correlated.

Attempts to overcome this limitation:

Bayesian networks, that combine Bayesian reasoning

with causal relationships between attributes

Decision trees, that reason on one attribute at the

time, considering most important attributes first


38/63

38

Bayesian Belief Networks

FamilyHistory

LungCancer

PositiveXRay

Smoker

Emphysema

Dyspnea

LC

~LC

(FH, S) (FH, ~S)(~FH, S)(~FH, ~S)

0.8

0.2

0.5

0.5

0.7

0.3

0.1

0.9


The conditional probability table

for the variable LungCancer


39/63

39


Bayesian belief network allows a subsetof the variables

conditionally independent

A graphical model of causal relationships

Several cases of learning Bayesian belief networks

Given both network structure and all the variables:

easy

Given network structure but only some variables

When the network structure is not known in advance


40/63

40

Classification by backpropagation


41/63

41

Neural Networks

Advantages

prediction accuracy is generally high

robust, works when training examples contain errors

output may be discrete, real-valued, or a vector ofseveral discrete or real-valued attributes

fast evaluation of the learned target function

Criticism

long training time difficult to understand the learned function (weights)

not easy to incorporate domain knowledge


42/63

42

A Neuron

The n-dimensional input vector xis mapped intovariable yby means of the scalar product and anonlinear function mapping

mk-

f

weighted

sum

Input

vectorx

outputy

Activation

function

weight

vector w

w0

w1

wn

x0

x1

xn


43/63

43

Network Training

The ultimate objective of training

obtain a set of weights that makes almost all the

tuples in the training data classified correctly

Steps Initialize weights with random values

Feed the input tuples into the network one by one

For each unit

Compute the net input to the unit as a linear combinationof all the inputs to the unit

Compute the output value using the activation function

Compute the error

Update the weights and the bias


44/63

44

Multi-Layer Perceptron

Output nodes

Input nodes

Hidden nodes

Output vector

Input vector:xi

wij

i

jiijj OwI

jIj eO

1

1

))(1( jjjjj OTOOErr

jkk

kjjj wErrOOErr )1(

ijijij OErrlww )(jjj

Errl)(


45/63

45

Classification based on concepts from

association rule mining


46/63

46

Association-Based Classification

Several methods for association-based classification

ARCS: Quantitative association mining and clusteringof association rules

It beats C4.5 in (mainly) scalability and also accuracy

Associative classification:

It mines high support and high confidence rules in the form ofcond_set => y, where y is a class label

CAEP (Classification by aggregating emerging patterns)

Emerging patterns (EPs): the itemsets whose supportincreases significantly from one class to another

Mine Eps based on minimum support and growth rate


47/63

47

Other Classification Methods


48/63

48

Other Classification Methods

k-nearest neighbor classifier

case-based reasoning

Genetic algorithm Rough set approach

Fuzzy set approaches


49/63

49

Instance-Based Methods

Instance-based learning: Store training examples and delay the processing

(lazy evaluation) until a new instance must beclassified

Typical approaches k-nearest neighbor approach

Instances represented as points in a Euclideanspace.

Locally weighted regression

Constructs local approximation

Case-based reasoning

Uses symbolic representations and knowledge-based inference


50/63

50

The k-Nearest Neighbor Algorithm

All instances correspond to points in the n-D space. The nearest neighbor are defined in terms of

Euclidean distance.

The target function could be discrete- or real- valued.

For discrete-valued, the k-NN returns the mostcommon value among the k training examples nearesttoxq.

Vonoroi diagram: the decision surface induced by 1-NN for a typical set of training examples.

.

_+

_ xq

+

_ _+

_

_

+

.

.

.

. .


51/63

51

Discussion on the k-NN Algorithm

The k-NN algorithm for continuous-valued target functions Calculate the mean values of the knearest neighbors

Distance-weighted nearest neighbor algorithm

Weight the contribution of each of the k neighbors

according to their distance to the query point xq

giving greater weight to closer neighbors

Similarly, for real-valued target functions

Robust to noisy data by averaging k-nearest neighbors

Curse of dimensionality: distance between neighbors couldbe dominated by irrelevant attributes.

To overcome it, axes stretch or elimination of the leastrelevant attributes.

wd xq xi

12( , )


52/63

52

Case-Based Reasoning

Also uses: lazy evaluation + analyze similar instances Difference: Instances are not points in a Euclidean space

Example: Water faucet problem in CADET

Methodology

Instances represented by rich symbolic descriptions(e.g., function graphs)

Multiple retrieved cases may be combined

Tight coupling between case retrieval, knowledge-based

reasoning, and problem solving Research issues

Indexing based on syntactic similarity measure, andwhen failure, backtracking, and adapting to additionalcases


53/63

53

Remarks on Lazy vs. Eager Learning

Instance-based learning: lazy evaluation Decision-tree and Bayesian classification: eager evaluation

Key differences

Lazy method may consider query instance xqwhen deciding how togeneralize beyond the training data D

Eager method cannot since they have already chosen globalapproximation when seeing the query

Efficiency: Lazy - less time training but more time predicting

Accuracy

Lazy method effectively uses a richer hypothesis space since it usesmany local linear functions to form its implicit global approximationto the target function

Eager: must commit to a single hypothesis that covers the entireinstance space


54/63

54

Genetic Algorithms

GA: based on an analogy to biological evolution Each rule is represented by a string of bits

An initial population is created consisting of randomlygenerated rules

e.g., IF A1 and Not A2 then C2 can be encoded as 100

Based on the notion of survival of the fittest, a newpopulation is formed to consists of the fittest rules andtheir offsprings

The fitness of a rule is represented by its classificationaccuracy on a set of training examples

Offsprings are generated by crossover and mutation


55/63

55

Rough Set Approach

Rough sets are used to approximately or roughlydefine equivalent classes

A rough set for a given class C is approximated by twosets: a lower approximation (certain to be in C) and an

upper approximation (cannot be described as notbelonging to C)

Finding the minimal subsets (reducts) of attributes (forfeature reduction) is NP-hard but a discernibility matrix

is used to reduce the computation intensity

Fuzzy Set


56/63

56

Fuzzy SetApproaches

Fuzzy logic uses truth values between 0.0 and 1.0 torepresent the degree of membership (such as usingfuzzy membership graph)

Attribute values are converted to fuzzy values e.g., income is mapped into the discrete categories

{low, medium, high} with fuzzy values calculated

For a given new sample, more than one fuzzy value may

apply Each applicable rule contributes a vote for membership

in the categories

Typically, the truth values for each predicted categoryare summed


57/63

57

Prediction


58/63

58

What Is Prediction?

Prediction is similar to classification

First, construct a model

Second, use model to predict unknown value

Major method for prediction is regression

Linear and multiple regression

Non-linear regression

Prediction is different from classification Classification refers to predict categorical class label

Prediction models continuous-valued functions


59/63

59

Predictive modeling: Predict data values or constructgeneralized linear models based on the database data.

One can only predict value ranges or category distributions

Method outline:

Minimal generalization Attribute relevance analysis

Generalized linear model construction

Prediction

Determine the major factors which influence the prediction Data relevance analysis: uncertainty measurement,

entropy analysis, expert judgement, etc.

Multi-level prediction: drill-down and roll-up analysis

Predictive Modeling in Databases

Regress Analysis and Log-Linear


60/63

60

Linear regression: Y = + X Two parameters , and specify the line and are to

be estimated by using the data at hand.

using the least squares criterion to the known values

of Y1, Y2, , X1, X2, . Multiple regression: Y = b0 + b1 X1 + b2 X2.

Many nonlinear functions can be transformed into theabove.

Log-linear models:

The multi-way table of joint probabilities isapproximated by a product of lower-order tables.

Probability: p(a, b, c, d) =abacadbcd

Regress Analysis and Log-LinearModels in Prediction


61/63

61

Locally Weighted Regression

Construct an explicit approximation to fover a local regionsurrounding query instance xq.

Locally weighted linear regression:

The target function fis approximated near xqusing thelinear function:

minimize the squared error: distance-decreasing weightK

the gradient descent training rule:

In most cases, the target function is approximated by a

constant, linear, or quadratic function.

( ) ( ) ( ) f x w w a x wnan x 0 1 1

E xq f x f x x k nearest neighbors of xq

K d xq x( ) ( ( )( ))

_ _ _ _( ( , ))

12

2

wj

K d xq x f x f x ajx

x k nearest neighbors of xq

( ( , ))(( ( ) ( )) ( )_ _ _ _


62/63

62

Prediction: Numerical Data

P di ti C t i l D t


63/63

Prediction: Categorical Data

Documents

7 Classification