Heuristic Algorithm Based on Rough Set Theory for Feature Selection
Chun Li · 2003
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.