A rough set approach to feature selection based on power set tree

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Traditional hill-climbing search approaches to feature selection have difficulties to find optimal reducts. And the current stochastic search strategies, such as GA, ACO and PSO, provide a more robust solution but at the expense of increased computational effort. It is necessary to investigate fast and effective search algorithms. Rough set theory provides a mathematical tool to discover data dependencies and reduce the number of features contained in a dataset by purely structural methods. In this paper, we define a structure called power set tree (PS-tree), which is an order tree representing the power set, and each possible reduct is mapped to a node of the tree. Then, we present a rough set approach to feature selection based on PS-tree. Two kinds of pruning rules for PS-tree are given. And two novel feature selection algorithms based on PS-tree are also given. Experiment results demonstrate that our algorithms are effective and efficient.