A Novel Feature Selection Heuristic Algorithm Based on Rough Set Theory
一种基于粗糙集启发式的特征选择算法

In this paper, a new feature measurement RMI (Ratio of Mutual Information)is presented based on the concept of rough set theory about certain set and uncertain set. Then a novel heuristic algorithm, MRMI-UC (Algorithm based on Maximal Ratio of RMI and Uncertainty Coefficient), is proposed for Feature Selection based on rough set theory. Firstly, the Core is obtained by discernible matrix and formed as a candidate feature subset. With the starting point of Core, the rest features are filtered iteratively to maximize both RMI and Uncertainty Coefficient. Finally the algorithm is tested on the UCI datasets, experiment results show that MRMI-UC is feasible and can find a good feature subset in most cases.