New Rough Set Attribute Reduction Algorithm based on Grey Wolf Optimization
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The document presents a new algorithm for feature selection that combines rough set theory with grey wolf optimization (GWO), aimed at reducing the number of features while maintaining high accuracy in data classification. It discusses the principles of rough sets and the GWO algorithm and shares experimental results demonstrating the effectiveness of this hybrid approach compared to traditional methods. The findings suggest that the GWO-based feature selection can outperform common methods like particle swarm optimization and genetic algorithms.
![Fitness Function
𝐹𝑖𝑡 = 𝛼 𝛾 𝑅 𝐷 + 𝛽
𝐶 −𝑅
𝐶
𝛼 ∈ [0,1] 𝛽 = 1 − 𝛼
𝑤ℎ𝑒𝑟𝑒 𝛾 𝑅 𝐷 is the classification accuracy of condition
attribute set R relative to decision D.](https://image.slidesharecdn.com/srgeroughgeryfeatureextractionapproch-151109070536-lva1-app6891/85/New-Rough-Set-Attribute-Reduction-Algorithm-based-on-Grey-Wolf-Optimization-7-320.jpg)
New Rough Set Attribute Reduction Algorithm based on Grey Wolf Optimization
- 1. 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
- 2. Agenda Introduction Rough Set Grey Wolf Optimization (GWO) The Proposed System of Rough Set and GWO Experimental Results Conclusions & Future Work
- 3. 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.
- 4. 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
- 5. 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.
- 7. Fitness Function 𝐹𝑖𝑡 = 𝛼 𝛾 𝑅 𝐷 + 𝛽 𝐶 −𝑅 𝐶 𝛼 ∈ [0,1] 𝛽 = 1 − 𝛼 𝑤ℎ𝑒𝑟𝑒 𝛾 𝑅 𝐷 is the classification accuracy of condition attribute set R relative to decision D.
- 8. 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
- 11. 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.
- 12. For further questions: Waleed Yamany Wsy00@fayoum.edu.eg