New Heuristic Attribute Reduction Algorithm Based on Sample Selection

Attribute reduction is one of the important topics in rough set theory.Heuristic attribute reduction algorithm with greedy strategy is one of the widely used approaches to compute reduction.Traditional heuristic algorithm uses all of the samples in the information system.However,it should be noticed that for a data set,different samples contribute different importance to find reduction,and it follows that the time efficiency of the heuristic attribute reduction algorithm suffers from the redundant samples.To solve such problem,a heuristic attribute reduction algorithm based on sample selection was proposed.The algorithm is composed of three stages.Firstly,the most informative samples are selected.Secondly,a new information system is formed by using these selected samples.Finally,one of the reductions can be computed by using heuristic algorithm.The experimental results show that the proposed algorithm can efficiently reduce the computational time.