A novel self-optimizing approach for knowledge acquisition

Attribute reduction and rule generation (attribute value reduction) are two of the main processes of knowledge acquisition. A self-optimizing approach based on a difference comparison table for knowledge acquisition for these processes is proposed. For the attribute reduction process, conventional logic computation was replaced by matrix computation with some added concepts from evolutionary computation and used to construct the self-adaptive optimizing algorithm. In addition, some sub-algorithms and proofs are presented in detail. For the rule generation process, a value orderly reduction algorithm, which simplifies the complexity of rule knowledge, is presented. The approach provides an effective and efficient method for knowledge acquisition, which is supported by the experimentation