一种结合粗糙集理论和启发式知识的特征选取算法

Heuristic Algorithm Based on Rough Set Theory for Feature Selection

通常数据库会包含很多冗余特征,找出重要的特征子集叫做特征选取。粗糙集理论提供了一种数学工具来发现所有可能的特征子集。但因为发现的特征子集的数目通常非常庞大,遍历所有子集将非常耗时。文章介绍一种算法,它结合了粗糙集理论和贪心的启发性知识来有效进行特征子集选取。

A database always contains a lot of attributes(sometimes instead of feature) that are redundant and not necessary for rule discovery. Feature selection is to find optimal feature subset. Rough set theory provides a mathematical tool that can be used to find out all possible feature subsets. Unfortunately, the number of possible subsets is always very large. Hence examining exhaustively all subsets of features is too time consuming. In this paper, we introduce an algorithm which is using rough set theory with greedy heuristics for feature selection.