Multi-agent Based Multi-knowledge Acquisition Method for Rough Set
The key problem in knowledge acquisition algorithm is how to deal with large-scale datasets and extract small number of compact rules.In recent years,several approaches to distributed data mining have been developed,but only a few of them benefit rough set based knowledge acquisition methods.This paper is intended to combine multiagent technology into rough set based knowledge acquisition method.We briefly review the multi-knowledge acquisition algorithm,and propose a novel approach of distributed multi-knowledge acquisition method.Information system is decomposed into sub-systems by independent partition attribute set.Agent based knowledge acquisition tasks depend on universes of sub-systems,and the agent-oriented implementation is discussed.The main advantage of the method is that it is efficient on large-scale datasets and avoids generating excessive rules.Finally,the capabilities of our method are demonstrated on several datasets and results show that rules acquired are compact,having classification accuracy comparable to state-of-the-art methods.
Attribute reduction Multi-agent technology Knowledge ac-quisition Classification accuracy
Yang Liu Guohua Bai Boqin Feng
Department of Computer Science and Technology,Xian Jiaotong University,Xian 710049,P.R.China; Scho School of Engineering,Blekinge Institute of Technology,Ronneby,372 25,Sweden Department of Computer Science and Technology,Xian Jiaotong University,Xian 710049,P.R.China
国际会议
成都
英文
140-147
2008-05-17（万方平台首次上网日期，不代表论文的发表时间）