An organizational co evolutionary algorithm for classification

An Organizational Co-evolutionary
Algorithm For Classification

Developed By: Badar Munir

National University of Computer & Emerging Sciences, Islamabad

Index
1. Abstract
2. Introduction
3. Reference Techniques
4. Proposed Technique
5. Results
6. Conclusion
7. Future idea


Abstract
OCEC is inspired from human interacting process.
- It uses the concept of Multi Poulation.
- It evolves individuals of population, individuals
that have same class arranges them in
organization.
Determines the fitness of each organization by
Calculating its
- Significance of each attribute.
- # of attributes in it


Abstract
- Rules are extracted when evolutionary process
ends.
- Generalized rules are by merging rules.
- OCEC performs better than other EA based
classification algorithms and has less
computational complexity.


Co-evolutionary Algorithm
- EA are based on the process Natural Selection.
- When ever it is applied on engineering
problems it gives satisfactory results.
- Co-evolutionary algorithm is Multi-Population.
- In Co-evolutionary algorithms individuals of
species-I competes/ cooperates with species-II.
Best individual from both them is selected and
copied to next generation.


Co-evolutionary Algorithm
Two types of Co-evolutionary algorithms are:
- Competitive
- Cooperative


Classification
Classification is a technique in which
• # possible inputs, #attributes in input,
• Range of attribute values
• Output Classes are already known.
NAME RANK YEARS TENURED
Mike Assistant Prof 3 no
Mary Assistant Prof 7 yes
Bill Professor 2 yes
Jim Associate Prof 7 yes
Dave Assistant Prof 6 no
Anne Associate Prof 3 no


Classification
- We divide the dataset into

Training Test
Data Data

Input Data


Classification
Classification
Algorithms
Training
Data

NAME RANK YEARS TENURED Classifier
(Model)
Mike Assistant Prof 3 no
Mary Assistant Prof 7 yes
Bill Professor 2 yes
Jim Associate Prof 7 yes
IF rank = ‘professor’
Dave Assistant Prof 6 no
OR years > 6
Anne Associate Prof 3 no THEN tenured = ‘yes’


Classification
Our aim in classification is to develop
- Generalized rules instead of Specific

Classification
Cases results during the evaluation of
classification:
Underflow & Overflow

Reference Techniques
1- Michigan Approach 9- XCS
2- Pittsburgh approach 10- GEP
3- Chonnei Algorithm 11- DMEL
4- GABIL Approach 12- EVOPROL
5- COGIN 13- SIA
6- JOINGA 14- ESIA
7- REGAL 15- EENCL
8- G-Net 16- EPNET


Michigan Approach
-Maintains a population of individual rules
which compete with each other for space and
priority in a population.
- It is not a good approach because it cannot
find best solution in complex problems instead
it converges rapidly.


Pittsburgh Approach
-Maintains a population of variable-length rule
set which compete with each other with respect
to performance on a domain task.
- computational cost for complex problems is
too high.


GABIL Approach
- GABIL continuously learns and refines
classification rules by interacting with
environment.
- For rules refinement it uses Genetic Algorithm


COGIN Approach
- CONGIN is a inductive approach that uses GA.
- It promotes Competitive or Predator type COE
between classification nichie’s.


JOINGA Approach
- CONGIN is a inductive approach that uses GA.
- It uses Cooperative or Symbiotic type COE
between classification nichie’s.
- It is used for Multi-Model classification.


REGAL Approach
- It is a distributed GA based approach designed
for learning first-order logic concepts
description from examples.


G-NET Approach
-G-NET is a descendant of REGAL that
consistently achieves better performance.


Organizational co-evolutionary (OCEC)
- OCEC copies COE model of Multiple
Populations
- It organizes the individuals in a sets called
organizations.
- Focusing on extracting rules from individuals &
organization.
- It does not focus on making organizations but
it focus on simulating interacting process among
organization.
- It is bottom-up approach.


Organizational co-evolutionary (OCEC)
- OCEC is based on organizations.
• Organization 1 • Organization 2

• Organization3 • Organization 4


Organization?
- An organization is a set of instances that have
same class
- Intersection between organizations is empty.
Org1 Π Org2 = Ø
Outlook Temp Humidity Wind Play
Sunny Hot High False No
Sunny Hot High True No
Overcast Hot High False Yes
Rainy Mild High False Yes
Rainy Cool Normal False Yes

* Each instance of an org is called Member of org.


Organization?
- If all members of org have the same value for
attribute A , then A is a Fixed-Value Attribute.
Suppose A’ is a ﬁxed-value attribute that satisfy
the conditions required for rule extraction, then
A’ is a Useful Attribute. The ﬁxed-value attribute
set of org is labeled as Forg, and the useful
attribute set is labeled as Uorg
- Useful attribute is significant because it
extracts rule.


Organization?
Wind  Forg1 & Uorg1 (Org2)
Outlook  Uorg2 (Org2)
Temp  Forg2 & Uorg2
Humidity  Forg2 & Uorg2

Rainy Cool Normal false Yes


Classification of Organizations
Classification of organizations are:
- Normal organization
- Trivial Organization
- Abnormal organization


Normal Organization
- It has more than one members
- Has non-empty useful attributes set

- It is denoted as ORGN


Trivial Organization
- It has only one members &
- All attributes of a member are useful.


- It is denoted as ORGT


Abnormal Classification
- It is an organization with empty useful
attributes.

Rainy Mild High True Yes

- It is denoted as ORGA


Organization Records
Organization keeps record of
- Member list
- Attribute type
- Organization type
- Member class
- Fitness of organization


Fitness of Organization
Fitness of an organization is calculated as:
- # of members
- # of useful attributes
-


Data Representation
OCEC can handle both
- Nominal &
- Continuous data



Knowledge Representation
- A is a set of attributes
- Each attribute has range of values.



- Instance Space I is the cartesian product of set
of attributes



- C is a set of classes
- Each member is



Rule Representation
Rules are represented in
IF <condition> THEN <class>
Each term in condition is triple:
Attribute, operator, value

* Rules are extracted when evolutionary process Ends


Working of (OCEC)
- OCEC during COE process generates a of set of
examples and at the end of COE it generates set
of rules.

if Temp = Mild and Outlook= Sunny
then Class = Play Tennis


Working of (OCEC)
- Inclusion or exclusion of attribute from a rule
depends upon the Significance of the attribute.
- EA Method is devised for determining the
Significance of the attribute.
- on the basis of attribute significance Fitness
function of organization is defined.


Working of (OCEC)
- EA Method is devised for determining the
Significance of the attribute.
- On the basis of attribute significance Fitness
function of organization is defined.


Evolutionary Operators (OCEC)
- Migrating Operator
- Exchanging Operator
- Merging Operator

Traditional operators such as mutation and
crossover are not used.


Migrating Operators (OCEC)
- 2 parent organizations are selected
- n members are selected from either parent
and are migrated to child’s
1 2 3 4 5 6 7 8

1 2 3 4 5 1 2 3


Exchanging Operators (OCEC)
- 2 org’s are randomly selected from Population
org1 & org2

Parent Parent
ORG1 ORG2

Child- Off-
ORGc1 ORGc2


- n members from each parent org1 are
randomly selected and exchanged
- Two child organization orgc1 & orgc2
1 2 3 4 5 6 7 8

1 6 7 8 5 1 2 3


- Two child organization orgc1 & orgc2
- Precondition is:
|orgp1|>1 and |orgp2|>1

1 ≤ n < MIN{|orgp1|, |orgp2|}


Merging Operators
- 2 org’s are randomly selected from Population
orgp1 & orgp2

Parent Parent
ORG1 ORG2

Child-
ORGc1


Merging Operators (OCEC)
- n members from each org1 are randomly
selected and merged.
- One child organization orgc1 & orgc2
1 2 3 4 5 6 7 8

1 2 7 8


Selection Operators (OCEC)
- Tournament Selection Mechanism is used.


Rule Extraction From Organization
-Rules are extracted from organizations when
Evolutionary process ends.
- Rules are extracted on the basis useful
attributes.
- Each useful attribute becomes TERM (part of
condition).
if temp=hot then play = yes


Performance Evaluation of OCEC
-Multiplexer problem
- Radar Target Recognition Problem.
-All results shows that OCEC has
- Higher prediction accuracy
- Low computational cost.


Scalability Evaluation of OCEC
-Scalability of OCEC is evaluated on synthetic
sets.
- trainging exampels increases from 1lac to 10
Million
- attributes are increases from 9 to 400.
- results shows that I achieves good scalability.


EVALUATION OF OCEC’S EFFECTIVENESS
A. Multiplexer Problems
o Multiplexer problems were introduced to the
machine learning community by Wilson in
1987, and have often been used to evaluate
the performance of learning classiﬁer
systems


B. Experimental Results
o The 20- and 37-multiplexer problems are used
o The training set of the 20-multiplexer
problem has 3000 examples, and that of the
37-multiplexer problem has 15 000 examples
o The test set of each problem has 100 000
examples
o The parameter N is set to 10% of the number
of the training set, and n


The evolutionary process of OCEC for the 20-multiplexer
problem


problem


Coding Output
problem


Coding Output


Comparison between OCEC & EA
- OCEC is based on organization while
traditional EA are based in individuals.
-OCEC has bottom-up searching mechanism
while EA has top-down searching mechanism
- the benefit of using organization is that I does
not generate meaningless rules.
- OCEC has higher prediction accuracy and low
computational cost.


Conclusion
- It is best tool for data mining.
- It has low computational cost
- It performs well in a complex, huge dataset of
individuals.
- On high scalability it performs better than
other techniques.


Future IDEA
-If we use a Floating Point Fitness Function then
it will give us better result in Scientific
applications.


An organizational co evolutionary algorithm for classification

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An organizational co evolutionary algorithm for classification

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