Incremental Algorithm for Attribute Reduction Based on Conditional Entropy
一种基于条件熵的增量式属性约简算法

Rough set theory is a mathematic tool to deal with incomplete and uncertain information, in which attribute reduction is one of important issues. The changing mechanism of condition entropy was analyzed when a new object was added to the original decision table. Based on this mechanism, a new incremental algorithm for attribute reduction was proposed. In this algorithm we divided the added objects into three cases. Furthermore, by these different cases incremental attribute reduces could be calculated quickly. At last, the validity of the proposed algorithm was depicted by an experiment.