Mutually Adaptive Distributed Heterogeneous Agents for Data Classification

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2007

Mutually Adaptive Distributed Heterogeneous

Agents for Data Classification

Joshua Alan Harlow

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Recommended Citation

Mutually

Adaptive

Distributed Heterogeneous

Agents

for Data

Classification

Master's Thesis Report

<[email protected]>

Department

Computer Science

Rochester Institute

Technology

Dr. Leon

Reznik,

Chair

Professor,

Department

Computer Science

Dr. Roger

Gaborski,

Reader

Professor,

Department

Computer

Science

Dr.

James

Heliotis,

Observer

ROCHESTER

INSTITUTE

OF TECHNOLOGY

The Undersigned Faculty Committee

Approves the Thesis

Mutually Adaptive

Distributed Heterogeneous Agents

for Data Classification

Dr. Leon Reznik

, Chairman

Department of Computer Science

Dr

. Roger Gaborski,

Reader

Department of Computer Science

Dr. James Heliotis, Observer

Department of Computer Science

Thesis/Dissertation Author Permission Statement

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Copyright 2007

by

Abstract

Efficient

data

is

the

fundamental

computer science

due

to

huge

important

As

such, there

have been

many

that

doing

form

data

using

large

of varied algorithms and architectures.

However,

few

that

implicitly

take

advantage of

the

distributed

has its

Therefore,

to

this

dilemma in

this thesis

posed of

mutually

distributed heterogeneous

designed,

implemented

and

experimentally

The

defined

distributed

chitecture

whereby

individual

to

tonomously

arbitrary

data

using

hierarchical

The

this

the

following:

follow hierarchical

cesses

to

form

autonomously

train, test,

work of other agents.

Through

it

found

that this

design improves

the

accuracy

to previously

implemented

architectures.

Furthermore

this

that the

tonomously

to previously

data

to

failures

little

Contents

1

Introduction

1

1.1

Distributed

2

1.2

Agent

4

1.3

K.D.D

7

2

Problem description

&:

9

2.1

Problem description

9

2.2

Research

\\

2.2.1

Outline

13

3

Background

^5

3.1

Agents

25

3.2

Classification

ig

3.2.1

Decision

trees

29

3.2.2

Neural

22

3.2.3

Clustering

24

4

Technologies

07

4.1

Java

4.2

XML

28

4.3

J.A.D.E

2g

4.4

Joone

34

4.5

Visualization

35

4.5.1

JFreeChart

36

4.5.2

JChart2D

36

4.5.3

Zeus J.S.C.L

37

4.6

Jakarta

37

5

Design

39

5.1

Architectural

39

5.1.1

Datasources

40

5.1.2

Supervisors &

42

5.1.3

Collectors

44

5.1.4

Receivers

45

5.1.5

Monitors

46

5.1.6

Applications

47

5.1.6.1

Agent

47

5.1.6.2

Neural

48

5.2

Implementation details

49

5.2.1

Applications

49

5.2.1.1

Data

49

5.2.1.2

Agent

52

5.2.1.3

Neural

53

5.2.2

Shared

53

5.2.2.1

Configuration

56

5.2.2.2

Protocols

56

5.2.2.3

Behaviours

59

5.2.3

Ontologies

59

5.2.3.1

External

60

5.2.3.2

Internal

61

5.2.4

Datasources

62

5.2.4.1

Data

62

5.2.4.2

Behaviours

63

5.2.4.3

G.U.I

64

5.2.5

Supervisors

64

5.2.5.1

Employment

64

5.2.5.2

Behaviours

68

5.2.5.3

G.U.I

69

5.2.6

Employees

70

5.2.6.1

Intelligence

70

5.2.6.2

Employment

72

5.2.6.3

Behaviours

75

5.2.6.4

G.U.I

76

5.2.7

Collectors

77

5.2.7.1

Proxy

77

5.2.7.2

Behaviours

7

5.2.7.3

G.U.I

79

5.2.8

Receivers

7g

5.2.8.1

Results

79

5.2.8.2

Behaviours

2

5.2.8.3

G.U.I

82

5.2.9

Monitors

82

5.2.9.1

Monitoring

g2

5.2.9.2

Behaviours

33

5.2.9.3

G.U.I

83

6

Experiments

85

6.1

Method

85

6.2

Design

86

6.2.1

Accuracy

86

6.2.2

Adaptability

87

6.2.3

Fault

tolerance

88

6.2.4

Data-sets

89

6.2.5

Agent

91

6.3

Results &

93

6.3.1

Accuracy

94

6.3.2

Adaptability

105

6.3.3

Fault

tolerance

113

7

Future

117

7.1

Agents

117

7.2

Data

118

7.3

Algorithms

119

8

Conclusions & implications

121

Appendices

A

User

123

A.l

Compiling

the

124

A.2

Configuring

124

A. 3

Launching

125

A.4

Launching

125

A. 5

Activating

126

A.5.1

Datasources

126

A.5.2

Supervisors

126

A.5.3

Employees

127

A.5.4

Collectors

127

A.

5.

5

Receivers

127

A.5.6

Monitors

128

A.

6

Examining

128

A.7

Additional

128

A. 7.1

Data

128

A.

7.2

Neural

129

A. 7.3

Waveform

129

A.

8

Further documentation

130

B

Interfaces

231

Chapter

1

Introduction

Intelligent

systems, containing

"intelligent

that

independent

in

dynamic,

[26]",

have

recently

become

increasingly

de

fast

growing

the

intelligence

These

have been developed for

in

diverse

applications,

varying

from

data

mining[41]

to security

intrusion

detection[l,

50].

Each

similar concepts

in that

intelligence

is taken

to

the

components

to

Furthermore

distributed

have

become

increasingly

popular, these

intelligent

are often also

distributed,

through

distributed

through

progressively

distribu

tion through

Additionally

input

usually

in

systems,

completely

partially

directed,

in many

principles

from

the

knowledge

discovery

in

databases

be

applied, whereby

"nontrivial

implicit previously

unknown,

potentially

information

(is extracted) from

Therefore,

these

form

the

backbone

this thesis

they

be introduced

in the

following

section,

allowing

the

heart

this

1.1

Distributed

systems

The

has

recently

been

shifting into

era,

"dis

tributed

have become increas

ingly

important.

These distributed

bring

many

including

reducing

the

impact

individual

failures, increasing

reliability

taking

social activities

to

in

being

With distributed

tecture

the

(see

figure

1.1(a))

model, the

hybrid

peer-to-peer model

(see

figure

1.1(c))

the

(see

figure

1.1(b))

the

is

distributed has

become

increasingly

One

mon attribute

that

is

is the

connections, whereby

through

a central server or

through

links

activity is

to

some

desired

goal,

this

be

n-directional protocol.

As

been

from

the

different

models, this

activity is

handled

differently by

the

distributed

(a)

Client-server

(b)

Pure

(c)

Hybrid

Figure

1.1:

Distributed

The

model, that is

distributed

is the

client/server systemshown

in

figure

1.1(a).

This

the

ity

by having

interact

in

to

other peers.

Notable

this

has

inherent

disadvantages, including

the

ofall resources at a

individual server,

the

ability

this type

to

deal

failures

Another fault

this

is

the

the

individual

servers, i.e. if

to

many

to

denial

may

result, whereby

do

the

desired

in

timely

(if

On

the

hand

this

is relatively

easy to

design

in that

only

be

the

that

its

be

from.

The

type

to

the

model,

figure

1.1(b),

is

distributed

that

lacks

the

the

These

types

of model

instead

completely

is

number of other peers who are aware of a number of other peers and so.

In

this

each peer can act as a client or server as needed

thereby

necessitating

This

has

popularity recently through the

such asbittorrent* and

distributed hash

table

implementations

corresponding routing

overlay

Pastry[52]

CAN[51].

Unfortunately

this

offering

inherently

limitless

scalability

potentially

has

with

organization, coherence,

security

tens,

hundreds

thousands

peers are

accessing

by

To

these

the third type

the

client-servermodel and

the

is

in

figure

1.1(c)

whereby

instead

being

completely

decentralized

this

hybrid

model offers a central peer which

plays

the

index

(similar

to

book

This index

centralized resourcewhere peers can search

for

discover

requiring

direct

to the

desired

The

index

this

by

storing

information

about each peer

(such

they

provide) but does

any

resources

therefore

maintaining

the

distributed

but

generating

less

traffic

security

needed

to

to

the

index

*http

//www

These

especially

to this thesis

the

in this

in

herently

distributed

the ability

to

easily

locate

and capabilities.

Since

the

location

is extremely

important

in

agent

system,

J.A.D.E.

the

framework

that this thesis

be

using,

be described

in

3

apply

using the

hybrid

in

figure

1.1(c).

This distributed

that

locate

other, simply

by

ensuring

they

their services,

languages

their

capabilities with

the

index

described

1.2

Agent

systems

To

how

is going

to

be

in this thesis

first

what

typically

defines

In

text

is

"computer

system

that

is

in

that

is

in

this

in

to

its

design

[63]. This

extremely

loose definition

describes

the

agent,

be

from figure

1.2.

From

this

figure

we can note

that this

take

input

from

it is

in

processing

to that

input,

therefore giving it

its

Thereafter

it

potentially modify the

by

performing

action

to

to

its sensory inputs.

f

f \

Agent

\

[Sens

t 01

:tion

\

itput]

V

Environment

^

Figure

1.2:

An

in its

[63]

From

this

definition

therefore

to

define

intelligent

is.

Unfortunately

the

description

exactly

describes intelligence

is

hotly

debated

in

the

A.I.

As

in

[64, 63]

likely

intelligent

has

that

it

autonomous, social,

Being

involve

the

described

in the

"basic

whereby

interact

with

humans

in

to

its

Secondly, having

characteristics would

involve

the ability to

interact

humans

in

agreed upon

language.

Thirdly

intelligent

be

in that

they

"perceive

their

in

timely

fashion

to

that

in it

in

to satisfy their

design

Finally

importantly

intelligent

agent must

be

in that it

"exhibit

behaviour

by

taking

the

initiative

in

to satisfy

(its)

design

Now

that the

definition

intelligent

has been

solidified, the

step in

understanding

is

determining

the

the

be

using

for

its in

ternal

There

traditionally

four base

[63],

these

being

logic

based,

based,

belief-desire-intention

based

layered

The logic

based

follows

the

traditional

whereby

takes

description

its

using its

behaviour

first-order

logic

[53]

its

therefore

changing

its

description. This

type

ofarchitecture

has

many

encompassing

benefits

being

theorized,

logically

clean,

tested

the

known

intelligence.

Unfortunately

though there

many

deter

this

from

being

in

platform,

having difficulty

representing

temporal

information

being

to

computationally

for

dynamic.

To

these

limitations

the

proposed, whereby

instead

of

the

the

logic based

is

(made

by Rodney

Brooks

[5]). This

architecture

simply

implements

behaviours

to

therefore

attempting

to

intelligent

behaviour

to

from

behaviours.

This

many

over

the

logic

based

architecture

being

simplistic,

tractability

having

failure.

Unfortunately

though this

has

its

problems as well

including being

to

learn from

being

limited

inherently

to

term

the

surrounding

The

third type

architecture

takes

completely different

how

process and manipulate

its

This

is

founded

two

impor

tant processes,

"deciding

to

(deliberation)

how

going to

these

(means-ends

Initially

these

formed

as

persisting

intentions,

then

direct

the

future

Then

once

these

type

is

in

to

these

goals,

may

involve

intentions

being

As

intuitive

this

modelabstracts

the

daily

in selecting

intentions,

to

achieve

these

intentions.

Unfortunately,

there

downsides;

down

side of

this

is the complexity

implementing

these

intentions,

goals,

correctly.

Output

...layer

Input .layer Output

Layer...

Layer3 Layer 2

Layer 1

....layer

Input

(a)

Horizontal

(b)

Vertical

Figure

1.3:

Layered

The fourth

final

deals

layers

whereby

flow

hierarchy

interacting

layers is

created, in

horizontal

Horizontally

layered

(see

figure

1.3(a)) directly

the

layers

the

input

the

potentially

may

have

internally

to the

externally

Vertically

layered

models

(see

figure

1.3(b))

the

hand have

input

handled

through passing

information

vertically

to

the

layer. These

models although

allowing

for

behaviours

to

concurrently

having

"conceptual

have

competing

behaviours.

Solving

this

requires an application of some

type

to

decide

behaviour

be

in

voked at

any

time,

therefore

increasing

design

complexity

any

interactions between

layers

be

handled

by

the

The importance

intelligent

to this thesis is

inherently

obvious,

but

the

ofarchitectures

is

inherent.

Throughout

5,

design decisions

be

made

that

layered

the

foundation

different

in

order

to

the

complexity

during

implementation.

1.3

Knowledge

discovery

in

databases

The knowledge

discovery

in

databases

(K.D.D.)

process,

be

from figure

1.4

an

important

in the

processing

data

"we

drowning

in

data,

but

starving

for

[28]. The

that

K.D.D.

have

the quality

the

data outputted,

selection,

preprocessing

transformation

(data

cleaning/conversion)

comprising

the

(60%

the

effort)

having

the

largest

impact

the

These

described here

briefly

from

beginning

to end,

selecting

large

in the

to

preprocessing

any

the

data-set

to

the

transformation

where

the

data

is

into

format for

the

data

mining

(such

string

to

transformations)

then to take

Thereafter

data-mining

algorithms

attempt

to

type

the

data,

potentially

performing

classification,

clustering,

identification

type

data

desired

by

the

users of

the

Once

this

has

in

large

data

be

generated,

hopefully

less

than the

input,

then

be

to

type

knowledge

(or

the

may

The

this thesis

is partially in the

first

three processes,

be

performed

before

the theses

be

Thereafter

these three

cesses arecompleted

this theses

then

that

data

to

its

on.

Subsequently

the

the

data-mining

(through

classification)

and attempt

to

in the

interpretation

K.D.D.

by displaying

progress and outputs

in

friendly

format

(see

5

B).

(^Selection')

(preprocessing

J

fTransformation

J

(

)

I

J

Data

Target data Preprocessed

Transformed

Patterns.

Figure

1.4:

K.D.D.

Chapter

2

Problem

description &

research objective

2.1

Problem description

Efficient

data

is

the

fundamental

puter science

due

to

huge

important

Over

many

both statistically

based

heuristics have been

developed, including

but

limited

to

k-means,

networks,

decision

tree

CART, C4.5,

ID3, ID4,

ID5R

(see

3).

Typically

these

in

which are not

distributed

statically

defined

data-sets[19,

56, 49,

3];

in

the

occurring

distributed

This

brings

it

the

data involved

in

is

dynamic,

the

distributed

low

to

failures,

thereby

making

it very

difficult

to

apply

Previously

these

have been

approached

by

using

fixed

to

the

surrounding

environment,

occurring

level.

Unfortunately,

the

design

this

is

that

these

isolated,

they

there

algorithms

to

together to

Concurrently

the necessity

for

framework

to these

has become

increasingly

relevant,

level

relevancy to

intrusion detection

depend

distributed

In

to

these capabilities, the

rithms can

be distributed

in

group in

form;

if

done

intelligently

has

the

to

improve

accuracy

Previous

have been

the

training

process,

have

the

to

be beneficial

(see

3),

but

there

does

currently

any

mutually

training

to

in

Therefore

to

the

previously

limiting

situations, this thesis

a solution

by

applying

mutually

training

has

design

that

mimics

the

the

brain,

is thought to

the

intelligence

This

is

the

Figure

2.1:

Neocortex

[8]

of

the

brain

familiar

with,

it is the

exterior

that is

in

(see

figure

2.1).

It

is

layers,

has

100

billion

cells,

1,000 to 100,000 synapses,

into

layers

that

has

the

thickness

These layers

thought to

functions,

different layers

interactions between layers

aiding in sensory perception,

reasoning,

vision,

touch,

balance,

[40]. These functions

are enabled

through

into

columns[25,

42],

forming

hierarchal

between

layers,

is

distributed

throughout

the

different

the

As

this

has been

extremely

flexible

and

adaptable, this thesis theorizes that

way

gaining these

is

to

follow

distributed hierarchy.

By following

this

design,

adaptability, tolerance to

failures,

for

flexibility by incorporating

into

disjoint but

intelligent

2.2

Research

objective

The

this

thesis

is to

develop

implementation

mutually

training

distributed

agents, employing research,

innovations

necessary

to

The

is

to

develop

the

methodology for

design

imple

mentation of a

framework

apply

working together to

efficiently classify

data,

to

variety

different

In

to

the

the

following

Develop

hierarchy

describing

the

func

tionality

Implement

the system, using the

algorithms,

framework

suitable

for

easy

to

different

Test

the

design

intrusion detection

identification data-sets

Compare

the

designed

accompanying

to

conventional

algorithms andmethodswhereverapplicable

The

be

forming

previously

hierarchy,

mimics

the

its

This

hierarchy

be

multiple

agents,

duties

that

the way the

the

work

together to

for

intelligent

behaviour.

In

to

this

objective

it

decided

that

design

be

follows

structure and

is

distributed

tasks,

the

tasks

forming

hierarchical

dependency

tasks

agents;

thereby

constituting

cooperative

hierarchy.

Supervising,

Classifying....

K.D.D.

i

\

i

/

processing

Hosting

Organizational

activities

Result Output

Knowledge

Figure

2.2:

Framework

The

to

these activities,

completed,

then

be

in

corporated

into

larger

framework

involving

data

hosting,

the

processes and result output

(as

be

in

figure

2.2).

Thereafter

be

the

necessary to invoke these algorithms, in

distributed

manner,

thereby

increasing

the

fault

tolerance

the

whole,

individual

failure

degradation

in the quality

(similar

to the

distributed

Although

the

this thesis

is to

develop

these

implementation,

additionally

friendly

ical

be

incorporated.

These

for

in-depth

knowledge

the

underlying

to

be

to

the

developed

and applications

(see

A).

The

hope

is

that

if this

is

done

intelligently,

structure

that

follows

hierarchal

to the neocortex, the

willproceedwith

increased

accuracy,

adaptability

tolerance

to

failures. This

solution, if successful,

therefore

have

immediate

impact

already

dynamic, distributed,

have

high failure

potential,

intrusion

detection

and waveform

identification.

Concurrently

the

developed

design, framework,

algorithms,

and

implementation

up

can

be

2.2.1

Outline

In

to

these

the

following

for

this thesis

be

conducted.

First

there

be

literature

topics

in the

area

in

this thesis

innovate

in

3.

This

mutually

training

development

discuss

if

how

they

to this thesis.

Following

this,

in

4,

be

the

technologies

to

in the

implementation

this

thesis,

including

ming

libraries.

Directly

this

be

the

design

functionality

details

which will

be

in

5.

This

into

the

high level

design,

lower level implementation details

the

to

complish

the

defined

Thereafter design

experiments,

and comparisons shall

be

in

6.

During

this chapter, the

to

be

be described

in

detail

along

the

by

running those

experiments, therefore

allowing for

this theses

Subsequently,

fol

lowing

this

be

future

being

in

7.

Finally

to

thesis

& implications

be discussed

in

8.

For

those

interested

in using the

developed

the

accompanying

G.U.I.

screenshots

in

A

B

be

These

to

learn how

to

the

to

be done

to

them up

and running.

Chapter

3

Background

As

"stand

the

(Isaac

Newton)

technologies

in

general

any

innovative

idea,

has

underlying

These

work

may

be

ideas

are

built

This

thesis

is

different,

follow

in this

chapter

that

have inspired

this work,

by having

flaws

this thesis

to

solve or

remedy

to

degree

by having

beneficial

idea

this thesis

apply

be

inspired

from.

3.1

Agents

One

the

first

mutually

learning

implementations

[62]

two

learning

labeled A.C.E.

(action

estimation)

A.G.E.

(action

group

estimation)

whereby

mutually

learning

in

The first

algorithm,

A.C.E.,

whereby

three

occur,

determination,

The first activity

determines

the

believes

it

carry

to

its

mental state and current

knowledge. Then

the

activity

whereby the

"compete for

the

to

become

actually

Thereafter

the

the

highest bid

is

assigning

[62]

to

the

future

The

algorithm,

A.G.E.,

is

to

A.C.E. but

the

by

competing

for

instead

solidarity

Thereafter the

bidding

assigning

occur,

but

instead

being

the

group

Following

this description the

their

results against a random search algorithm

using both

to

blocks

in

the traditional

blocks

(choosing

randomly

through the

Their

that the

learning

significantly

better

to

learn

This

therefore positively

the

ideology

that

working

together,

those

reactive,

better

than

choosing

In

[57],

the

Ming

Tan

investigates

"given

the

learning

agents,

independent

do

during

Using

learning[33]

the

to

de

termine

the

independent

identify

any

trade-offs

may

Therefore

learning

typically

q-learning

[61]

the

to

the

acting in

hunter-prey

In

this

is setup to

be

hunter

prey,

limited

depth

field,

location.

To

implement

policy

the

hunter

group

prey

they

+1

reward,

they

but

do

prey

they

Through

tests

the

empirically

determines

that

learn faster

than

independent

The

that

the

for

this

cost, providing

tradeoffs

this thesis

In

[24, 23]

the

learning

whereby

the

first

through

training

"are

then

to

their

by

generalizing

they

have

Using

teacher-learner model, the

a

trainer/teacher

instructs

on

how

to

behave.

In

this

agent can

be

trainer

learning

agent,

the trainer tries to train the

learner

for

its

own

benefit. The

trainer

the

learning

behaviours,

through

feedback

mechanism which

therefore

the

learning

The learner

subsequently

its

level based

the

state,

the

and

its

feedback

to

determine

to

to

increase

its

level.

In

the

the

apply their algorithm, comparing

their

randomly

using

traffic

light

Through

results

the

to

showing that

able

to

0%

in

49%

the cases,

the

downside

that

a

large

In

[45, 44]

the authors,

Luis Nunes

Eugenio Oliveira

to

determine "how

can agents

benefit from exchanging information

during

Interesting

in this

papers

is that the

the

possibility

that the

may

have different

tures

different

learning

algorithms, therefore

being

homogenous

In

to

to

determine

the

to this

important

question, the

erate agents

whereby

different

learning

(random

walk,

evolutionary

algorithms,

annealing

q-learning)

Similarly

[24, 23]

the

authors use a

traffic

light

simulation, whereby the

"observe

the

(their)

for

their

local

decide

the

to

to the

North

and

South

During

this

the

by

broadcasting

their

best

for

whereby then

the

beginning

the

i

to

determine

has

the

highest

to then

seek advise

from. Thereafter

the

sends

its

to the

who

then

that

using its

learning

algorithm, therefore giving

feedback

to the

After

running many

trials

the

learn

ing

the

determined

that

"advice-exchange

to

be

promising way

in

from

interaction

during

the

learning

but

they

also

found

that

"bad

advice,

blind

reliance,

hinder

the

learning

process,

beyond

In

[32],

the

the

communication,

has "ac

cess

to

local information

through

learns

to

only the

information that is

In

to

the

munication

the

develop

predator-prey simulation, whereby the

is to

have

four

prey

four

in

To

for

munication

to

be

the

develop

blackboard

[9]

like

whereby

all agents post

binary

(messages

only containing

{0, 1})

to

board.

Therefore

using this

board

the

develop

the

by

applying

genetic algorithms

[22]

to

different

agents; whereby

different

languages

Applying

these

through

life-cycles,

the

determined

that

"genetic algorithm(s)

communicating

that

form

the

best

non-communicating predators,

that

increasing

the

language

improves

These

to the

cooperative

learning

is

beneficial

achievable,

they

that

cooperating

communicating

together

do

better

than

independent

This

is imperative

for

this thesis

the

in the

implementation

this thesis

be

inherently

communicating

cooperating to

progress.

Also

these

bring

forward

this thesis

be

the

effect of

blind

the

hinder

this

theses

3.2

Classification

As far

concerned,

there

have been

large

different

algorithmsover

the

20

have

to

define

for

These different

large

intelligence

decision

trees,

rule

induction,

As

the

this thesis

is

to

dynamic

framework

using agents,

these

works _{and associated methods will provide a sense of contrast}

/comparison

most

do

easily

distributable,

adapting

for data

As for

the

algorithmswhich

do

adapting

data

classification,

these

be

in the

design

(see

5).

Additionally

in

6

will

be

allowing

for

comparison, if possible,

the

this

thesis

with

previously

implemented

3.2.1

Decision

trees

One

the

for

generating

has been

to

decision

trees

for

the

instances

being

These decision

trees

like

whereby

each node

in the tree

type

test that

the

tree

into

leaf

assigning

These decision

trees

frequently

for

they

"divide

strategy

for

[58]

One

the

known

for

building

these trees is the

ID3

attempts

to

information

entropy

defined

in

[55]

information

to

determine

the

of

the

data

level

the tree.

On

basic level

the entropy

calculation,

in

3.1,

determines

the

information

by

by being

S

the

I in

S.

Similarly

the

information

shown

in

3.2

to

determine how

data-set

the

data-set into different

This

through the

the entropy

formula,

but

taking

into

Sv

the

the total

data-set

for

A

has

therefore

determining

the

that this

A

The ID3

takes

training

instances

recursively

selects attributes of

those

training

instances,

calculating their entropy

the

the

the

tree

This

the

attributes not partitioned until no more

features

to

the

perfectly

the

training

instances.

As

be

inherently

this

is

non-iterative,

in that

data

is

the

tree

be

from

Entropy(S)

-?(/)

log2(p(7))

(3.1)

Gain(S,

A)

Entropy(S)

W

f^\

Entropy

(Sv)\

(3.2)

Another

decision

tree algorithm,

C4.5[49]

builds

the

ID3

algorithm

by

providing

These improvements include

allowing

known

missing

values, tree

pruning to

the tree size, attempting to

over-fitting,

handling

(i.e.

time)

generally

improving

This

providing

many

for

has limitations

in that its pruning

by

the

(Ross

Quinlan)

being

"heuristic

with questionable

underpinnings."

The

C4.5

by

being

ID3 does

for

iterative tree

building,

therefore

limiting

its

to

data is

being

the

time to

the

tree

is

inexpensive

be

a concern.

Another

decision

tree

is

for

ing

decision

trees

is

CART[4]. This

different

than

ID3,

whereby

instead

information

is

instead.

The CART

has

strengths[65]

C4.5

ID3

it

deal

data

high

sionality,

handle

missing

is

be

by

Unfortunately

it

has

[65]

it is

iterative

the

in

CART

required

to

follow

any

distribution.

In

to

for iterative

tree

building,

few

building

ID3

have been

One

the

first

the

ID4

in

[54]

whereby the

test

This

is

updated as new

instances

in

shifting

test

lower

scoring

nodes get replaced with

higher

scoring

Unfortunately

this

discards

the

lower

scoring

they

therefore causing

movement and

lose

(and

the

information

they

contain)

the

tree

to

These

this

if the tree

[58]

A

to

ID4's

in

[58]

whereby the

labeled

ID5R.

This

the

ID4

and goes

into depth

theoretically

empirically

how

their

the

basic

methods of

ID4

to

for

restructuring

This

is

by

the

following

method; instead

discarding

this

"pull-up,

tree

that

consistency

the

instances.

Overall

this

algorithm,

through experiments,

found

to

be less

for

than

applying the

ID3

repeatedly

the technical

problems which were

inherent

the

ID4

A

third

iterative

in

[59]

slightly different methodology

to

iterative

tree

building

whereby

mapping

from

tree

to

tree

is generated,

using

test

information

ID4

ID5R

This

labeled I.T.I, for

iterative

tree

induction

the

following

procedure[59].

As

instance

arrives, it is

down

to

the

branches

leaf

is reached,

updating

through the test

information

marking that

After

this

instance

has been incorporated

the

modify the test

that

to

the

changes

that

instance

may

have

This

its slightly

presented

in the

through

that

to

C4.5,

accuracy

is

to

better.

Unfortunately

the

updating

data-sets

corresponding

instances

large

found

to

be

extremely

3.2.2

Neural

networks

A

for generating

is

using the

neural networks

to

internalize

the

[30];

of

their many

These

the

neuron structure

that

the

As

neurons

(see

figure

3.1(b)) directly

map to

biological

(see

figure

3.1(a))

both have

a similar structure and perform

many

the

These biological

computational responsibilities

include

being

the

processing

transmit

ters

information

in

animals[31]

they

These

through

using

interactions

for biological

for

complicated structure and various algorithm are used

to

the

biological

processes.

As

the topic

books

the

and

the

background

be

in the

following

The biological

from

terminals whereby

released which

then

through

the

boundary

potentially

by

another neurons

dendrites.

Then through

involving

the

transmitted

the

in ionic

balance

inside the

these

may

the

the receiving neuron, causing

chemicals

to

through the

terminals.

It

is through this

process

that

initially

inspired the

development

the

its

(a)

Biological

structure[31]

O.-_Activationvalue of unit_j

W-Weightonthelink betweenMoron Iand_J

Function '^"a

(b)

Artificial

Figure

3.1:

Neuron

complicatednetwork of

interactions. These

follow

but

procedure, whereby

logical

together through

different

types

The input

to

then

in

some manner and placed

through

function

to

is then

logically

to

receiving

Depending

the

different

type

model

being

the

type

data

being

these

functions

can

vary

from

function

(see

figure

3.2(a))

to

hyperbolic

tangent

function

(see

figure

3.2(b))

to

function

(see

figure

3.2(c))

to

functions

that modify

the

inputs in

As biological

to changes,

also

inherently

to

in the

input data. This

learning

through

supervision and reinforcement of a set of

training

data

the

that

f