A Logarithmic Weighted Algorithm for Minimal Test Cost Attribute Reduction

  Minimal test cost attribute reduction is an important problem in cost-sensitive learning since it reduces the dimensionality of the attributes space.To address this issue,many heuristic algorithms have been used by researchers,however,the effectiveness of these algorithms are often unsatisfactory on large-scale datasets.In this paper,we develop a logarithmic weighted algorithm to tackle the minimal test cost attribute reduction problem.More specifically,two major issues are addressed with regard to the logarithmic weighted algorithm.One relates to a logarithmic strategy that can suggest a way of obtaining the attribute reduction to achieve the best results at the lowest cost.The other relates to the test costs which are normalized to speed up the convergence of the algorithm.Experimental results show that our algorithm attains better cost-minimization performance than the existing a weighted information gain algorithm.Moreover,when the test cost distribution is Normal,the effectiveness of the proposed algorithm is more effective for dealing with relatively medium-sized datasets and large-scale datasets.

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