A feature selection method based on instance learning and cooperative subset search - Xiaoyuan XU; Li HUANG (Dianxin kexue)
Feature subset selection is a key problem in such data mining classification tasks.In practice,the filter methods ignore the correlations between genes which are prevalent in gene expression data,additionally,existing methods are not specially conceived to handle the small sample size of the data which is one of the main causes of feature selection instability.In order to deal with these issues,a new hybrid,filter wrapper was proposed,and a cooperative subset search(CSS),was then researched with a classifier algorithm to represent an evaluation system of wrappers.The method was experimentally tested and compared with state-of-the-art algorithms based on several high-dimension allow sample size cancer data sets.Results show that the proposed approach outperforms other methods in terms of accuracy and stability of the selected subset.