基于增强蜂群优化算法的特征选择算法

doi:10.11772/j.issn.1001-9081.2016.05.1307

计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1307-1312.DOI: 10.11772/j.issn.1001-9081.2016.05.1307

基于增强蜂群优化算法的特征选择算法

张霞^1,2,3, 庞秀平³

1. 河北经贸大学计算机中心, 石家庄 050061;
2. 北京科技大学信息工程学院, 北京 100083;
3. 河北经贸大学经济管理学院, 石家庄 050061

收稿日期:2015-11-11 修回日期:2016-01-11 出版日期:2016-05-10 发布日期:2016-05-09
通讯作者: 张霞
作者简介:张霞(1975-),女,河北深泽人,副教授,博士,主要研究方向:数据挖掘、电子商务;庞秀平(1966-),男,河北泊头人,副教授,主要研究方向:电子商务。
基金资助:
国家自然科学基金重点项目(61333002);河北省科技计划项目(14457419D)。

Feature selection algorithms base on enhanced bee colony optimization algorithm

ZHANG Xia^1,2,3, PANG Xiuping³

1. Computer Centre, Hebei University of Economics and Business, Shijiazhuang Hebei 050061, China;
2. School of Information Engineering, University of Science and Technology Beijing, Beijing 100083, China;
3. College of Economics & Management, Hebei University of Economics and Business, Shijiazhuang Hebei 050061, China

Received:2015-11-11 Revised:2016-01-11 Online:2016-05-10 Published:2016-05-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61333002), the Science and Technology Planning Project of Hebei Province (14457419D).

摘要/Abstract

摘要： 针对传统蜂群优化(BCO)算法探测能力强但搜索能力较弱的问题,提出一种搜索能力增强的BCO算法,并将其应用于数据特征选择问题以提高特征选择的性能。首先,为食物源引入全局权重的概念,用以评估各食物源对种群的重要性,降低蜂群搜索的随机性;然后,设计了两步筛选的招募方法提高蜂群搜索能力并保持多样性;最终,为食物源引入局部权重的概念,用于评估某个食物源与类标签的相关性,从而优化解特征选择问题。仿真实验结果表明,所提方法可以明显提高BCO的优化效果,同时获得了较好的特征选择效果,并且优于基于差异的人工蜂群算法(DisABC)和蜂群优化特征选择算法(BCOFS)。

关键词: 特征选择, 蜂群优化算法, 食物源, 类标签, 全局权重, 招募算法

Abstract: Concerning the problem that the traditional Bee Colony Optimization (BCO) has good exploration but week exploitation performance, an exploitation enhanced BCO algorithm was proposed, and applied to data feature selection problem in order to improve the performance of the feature selection. Firstly, global weight was introduced into the food source, and was used to evaluate the importance of each food source to population, thus the randomness of exploitation was reduced; then, a recruiting method with two-step filtering was designed to improve the exploitation performance and keep diversity; at last, local weight was introduced into the food source to evaluate the correlation between the food source and class labels which were used in the feature selection model. Simulation experimental results show that the proposed method can improve the effect of the BCO and get a good performance in the feature selection problem, and the method outperforms Dissimilarity based Artificial Bee Colony (DisABC) and Feature Selection based on Bee Colony Optimization (BCOFS).

Key words: feature selection, Bee Colony Optimization (BCO) algorithm, food source, class label, global weight, recruiting algorithm

中图分类号:

TP18

张霞, 庞秀平. 基于增强蜂群优化算法的特征选择算法[J]. 计算机应用, 2016, 36(5): 1307-1312.

ZHANG Xia, PANG Xiuping. Feature selection algorithms base on enhanced bee colony optimization algorithm[J]. Journal of Computer Applications, 2016, 36(5): 1307-1312.

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基于增强蜂群优化算法的特征选择算法

Feature selection algorithms base on enhanced bee colony optimization algorithm

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