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

IntroductionProblem DefinitionMotivation

Background Proposed ApproachResults and DiscussionConclusion and Future Works

SRGE Workshop, Bio-Inspiring and Evolutionary Computation Trends: Applications and Open Issues (7-November-2015)

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Introduction

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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.

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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.

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Background

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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.

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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

intelligence of creatures.

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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.

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Proposed

Approach

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Proposed Approach11

+

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 Discussion13



Visualization of 1000 rules


Conclusion and Future Works

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Conclusion

In this study, a hybrid method for breast cancerdiagnosis based on RS for feature selection and GAmetaheuristic is presented.

The hybrid proposed method exhibits consistent andbetter performance than the RS classifier only.

The hybrid proposed method provides the expert by apromising tool to extract knowledge visually andinstantly.

The hybrid proposed method represents anindependent phase for classification and reductionwhich can recover previous steps defects.

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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.

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Thanks and Acknowledgement24


rules reduction using evolutionary meta-heuristics

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