Feature Selection Based on Difference and Similitude in Data Mining  

作　　者：WU Ming YAN Puliu 

机构地区：[1]School of Electronic Information, Wuhan University, Wuhan 430072, Hubei, China

出　　处：《Wuhan University Journal of Natural Sciences》2007年第3期467-470,共4页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (90204008);Chen-Guang Plan of Wuhan City(20055003059-3)

摘　　要：Feature selection is the pretreatment of data mining. Heuristic search algorithms are often used for this subject. Many heuristic search algorithms are based on discernibility matrices, which only consider the difference in information system. Because the similar characteristics are not revealed in discernibility matrix, the result may not be the simplest rules. Although differencesimilitude（DS） methods take both of the difference and the similitude into account, the existing search strategy will cause some important features to be ignored. An improved DS based algorithm is proposed to solve this problem in this paper. An attribute rank function, which considers both of the difference and similitude in feature selection, is defined in the improved algorithm. Experiments show that it is an effective algorithm, especially for large-scale databases. The time complexity of the algorithm is O（｜ C ｜^2｜U ｜^2）.Feature selection is the pretreatment of data mining. Heuristic search algorithms are often used for this subject. Many heuristic search algorithms are based on discernibility matrices, which only consider the difference in information system. Because the similar characteristics are not revealed in discernibility matrix, the result may not be the simplest rules. Although differencesimilitude（DS） methods take both of the difference and the similitude into account, the existing search strategy will cause some important features to be ignored. An improved DS based algorithm is proposed to solve this problem in this paper. An attribute rank function, which considers both of the difference and similitude in feature selection, is defined in the improved algorithm. Experiments show that it is an effective algorithm, especially for large-scale databases. The time complexity of the algorithm is O（｜ C ｜^2｜U ｜^2）.

关 键 词：knowledge reduction feature selection rough set difference set similitude set attribute rank function 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]