Feature Selection Approach Based on Whale Optimization Algorithm

被引：0
|
作者：
Sharawi, Marwa [1 ]
Zawbaa, Hossam M. [1 ,2 ]
Emary, E. [1 ,2 ]
Zawbaa, Hossam M. [1 ,2 ]
机构：
[1] Arab Open Univ, Fac Comp Studies, Cairo, Egypt
[2] Beni Suef Univ, Fac Comp & Informat, Bani Suwayf, Egypt
来源：
2017 NINTH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTATIONAL INTELLIGENCE (ICACI) | 2017年
关键词：
nature-inspired optimization; whale optimization; feature selection; PARTICLE SWARM OPTIMIZATION; CLASSIFICATION; PSO;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a feature selection system is introduced applies the whale optimization algorithm (WOA). WOA is a recently introduced meta-heuristic optimization algorithm that mimics the natural behavior of the humpback whales. The proposed model applies the wrapper-based method to reach the optimal subset of features. This technique was applied to find the best feature subset that maximizes the accuracy of the classification while preserving the minimum number of features. The proposed model is compared with the particle swarm optimization (PSO) and genetic algorithm (GA) using a number of assessment indicators on 16 different data-sets from UCI data repository. The results demonstrate the advantage of the introduced algorithm compared to the other optimizers.
引用
收藏
页码：163 / 168
页数：6
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