Pattern classification with granular computing

This paper puts forward new approaches to solve the pattern classification problems by using granularity computing of quotient space theory. Through learning the training samples reorganized by different granularities, the difficulty and complexity of the learning is reduced, and the classification accuracy is improved greatly. Moreover granular computing is used to solve the classification problems with incomplete information system in this paper. Since the granular computing methods are accords with the human cognitive custom, they can improve the quality of classification algorithm effectively. The adoption of this approach will largely expand the extent of various classification algorithms. The detailed procedures of these two methods based on granular computing and their corresponding experimental results are present which validate our proposed methods highly.