Rules Reduction using Evolutionary Meta-Heuristics

Rules Reduction using
Evolutionary Meta-Heuristics
*Hanaa Ismail Elshazly and Aboul Ella Hassanien
http://www.egyptscience.net
*Faculty of Computers and Information, Cairo University, and SRGE member
Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of
Computers and Information, Cairo University

Overview
 Introduction
Problem Definition
Motivation
 Background
 Proposed Approach
 Results and Discussion
 Conclusion and Future Works
SRGE Workshop, Bio-Inspiring and Evolutionary Computation Trends: Applications and Open Issues (7-November-2015)
2

Introduction
3

Problem Definition
 Decision rules suffer of some shortcomings like
 Difficulty of knowledge acquisition process.
 Big size of rules that need maintenance.
 More cost development and time from experts and
knowledge engineers.
 The need for an automatic system for
good decision rules selected will offer
great help and will reduce cost and effort
for knowledge base construction and
rules maintenance.
4

Motivation In Biomedical Field
 Medical diagnosis depends on the
experience of the physician, Decision
rules can transfer this experience.
 Decision rules present an easy and
strong method of inference consistent
with expert knowledge and ability of
expression and explanation.
 Decision rules can be easily adapted
due to its declarative representation.
5

Background
6

Background
 Meta-Heuristic Optimization Techniques
have been applied in various fields of
study.
 It can divided into Single-solution-based and
Population-based.
 All meta-heuristic techniques divided the
search space into two phases : exploration
and exploitation.
7

Background
 Meta-Heuristic Optimization techniques can be
divided into :
 Evolutionary Concepts : Inspired from evolutionary processes and
natural operations like crossover, mutation and selection, GA most
popular.
 Physical Phynomena : Random set of search agents
communicate and move throughout search space according to physical
rules like gravitational force and ray casting.
 Animal Behaviours : Imitate social behavior of swarms, herds,
schools of creatures in nature, using the simulated collective and social
8

Background
 GA simulates Darwnian evolution concepts.
 Optimization is done by evolving an initial random
solution.
 Each new population is created by the combination
and mutation of the individuals in the previous
generation.
 Best individuals are chosen and participate in
generating new population.
9

Proposed
Approach
10

Proposed Approach
11
+
Preprocess
Normalized Data
Breast Cancer
Data
200 Reducts &
100 000 Rules
RS
Evaluation
Process
Selected Rules
Evolution
algorithms
Reduced Rules
Selected Reduct
Generate
Rules
Rules
Rendring
Classified
Instances

Results and
Discussion
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Results and Discussion
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14

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 Visualization of 1000 rules

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Conclusion and Future
Works
21

Conclusion
 In this study, a hybrid method for breast cancer
diagnosis based on RS for feature selection and GA
metaheuristic is presented.
 The hybrid proposed method exhibits consistent and
better performance than the RS classifier only.
 The hybrid proposed method provides the expert by a
promising tool to extract knowledge visually and
instantly.
 The hybrid proposed method represents an
independent phase for classification and reduction
which can recover previous steps defects.
22

Future Works
 More biomedical data sets with various
dimensionality will be tested.
 Search for unique criterions within each
medical domain to improve selection rules
process visualization.
 Test other optimization techniques for rules
reduction.
23

Thanks and Acknowledgement
24

Rules Reduction using Evolutionary Meta-Heuristics

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