Global Best Artificial Bee Colony for Minimal Test Cost Attribute Reduction

  The minimal test cost attribute reduction is an important component in data mining applications,and plays a key role in cost-sensitive learning.Recently,several algorithms are proposed to address this problem,and can get acceptable results in most cases.However,the effectiveness of the algorithms for large datasets are often unacceptable.In this paper,we propose a global best artificial bee colony algorithm with an improved solution search equation for minimizing the test cost of attribute reduction.The solution search equation introduces a parameter associated with the current global optimal solution to enhance the local search ability.We apply our algorithm to four UCI datasets.The result reveals that the improvement of our algorithm tends to be obvious on most datasets tested.Specifically,the algorithm is effective on large dataset Mushroom.In addition,compared to the information gain-based reduction algorithm and the ant colony optimization algorithm,the results demonstrate that our algorithm has more effectiveness,and is thus more practical.

作者单位: Lab of Granular Computing,Minnan Normal University, Zhangzhou 363000, China
母体文献: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)论文集
会议名称: The 9th International Conference on Rough Sets and Knowledge Technology (RSKT 2014)(第九届粗糙集与知识技术国际会议)
会议时间: 2014年1月1日
会议地点: 上海
主办单位: 中国人工智能学会,同济大学
语 种: eng
在线出版日期: 2015年8月31日