New Rough Set Attribute Reduction Algorithm based on Grey Wolf Optimization

New Rough Set Attribute

Reduction Algorithm based on Grey Wolf Optimization

Waleed Yamany* and Aboul Ella Hassanien

*Faculty of Computers and Information, Fayoum University and SRGE Member

Egyptsceince.net Bio-inspiring and evolutionary computation: Trends, applications and open issues workshop, 7 Nov. 2015 Faculty of

Computers and Information, Cairo University

Agenda Introduction Rough Set Grey Wolf Optimization (GWO) The Proposed System of Rough Set

and GWO Experimental Results Conclusions & Future Work

Introduction Feature selection is one of the most essential problems in

the fields of data mining, machine learning and pattern

recognition.

The main purpose of feature selection is to determine a

minimal feature subset from a problem domain while

retaining a suitably high accuracy in representing the

original features.

Rough Set Rough set theory can transact with uncertainty and vagueness in

data analysis. It has been widely applied in many fields such as

data mining, machine learning,.

Rough set theory provides a mathematical tool to find out data

dependencies and reduce the number of features included in

dataset by purely structural method.

It is a formal approximation of a crisp set in terms of a pair of

sets which give the lower and the upper approximation of the

original set

Grey Wolf Optimization (GWO)

Grey wolf optimizer (GWO) is a population based meta-heuristics algorithm simulates the leadership hierarchy and hunting mechanism of gray wolves in nature .

We consider the fittest solution as the alpha , and the second and the third fittest solutions are named beta and delta , respectively.

In the mathematical model of hunting behavior of grey wolves, we assumed the alpha , beta and delta have better knowledge about the potential location of prey.

Fitness Function

We implement the BA-RSFS feature selection

algorithms in MatLab 7.8. The computer used to

get results is Intel (R), 2.1 GHz CPU; 2 MB

RAM and the system is Windows 7 Professional.

The dataset used for experiments were

downloaded from UCI- Machine Learning

Repository.

Experimental Results:Specifications of Used Computer

Experimental Results:

Experimental Results:

Conclusions The goal of this paper was to propose a hybrid GWO with Rough set

feature selection method to select a smaller number of features and

achieving similar or even better classification performance than

using all features.

GWO proves performance advance in both classification accuracy

and feature reduction over common methods such as PSO and GA.

For further questions:

Waleed YamanyWsy00@fayoum.edu.eg