基于样本选择的启发式属性约简方法研究

属性约简是粗糙集理论的核心研究内容之一。借鉴于贪心策略的启发式算法是求解约简的一种有效技术手段。传统的启发式算法使用了决策系统中的所有样本,但实际上每个样本对约简的贡献程度是不同的,这在一定程度上增加了启发式算法的时间消耗。为解决这一问题,提出了一种基于样本选择的启发式算法,该算法主要分为3步:首先从样本集中挑选出重要的样本；然后利用选取出的样本构建新的决策系统；最后利用启发式算法求解约简。实验结果表明,新算法能够有效地减少约简的求解时间。

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.