An incremental approach for attribute reduction based on knowledge granularity

Abstract

Rough set provides a theoretical framework for classification learning in data mining and knowledge discovery. As an important application of rough set, attribute reduction, also called feature selection, aims to reduce the redundant attributes in a given decision system while preserving a particular classification property, e.g., information entropy and knowledge granularity. In view of the dynamic changes of the object set in a decision system, in this paper, we focus on knowledge granularity-based attribute reduction approach when some objects vary dynamically. We first introduce incremental mechanisms to compute new knowledge granularity. Then, the corresponding incremental algorithms for attribute reduction are developed when some objects are added into and deleted from the decision system. Experiments conducted on different data sets from UCI show that the proposed incremental algorithm can achieve better performance than the non-incremental counterpart and incremental algorithm based on entropy.