Binary Classification of Multigranulation Searching Algorithm Based on Probabilistic Decision

Multigranulation computing, which adequately embodies the model of human intelligence in process of solving complex problems, is aimed at decomposing the complex problem into many subproblems in different granularity spaces, and then the subproblems will be solved and synthesized for obtaining the solution of original problem. In this paper, an efficient binary classification of multigranulation searching algorithm which has optimal-mathematical expectation of classification times for classifying the objects of the whole domain is established. And it can solve the binary classification problems based on both multigranulation computing mechanism and probability statistic principle, such as the blood analysis case. Given the binary classifier, the negative sample ratio, and the total number of objects in domain, this model can search the minimum mathematical expectation of classification times and the optimal classification granularity spaces for mining all the negative samples. And the experimental results demonstrate that, with the granules divided into many subgranules, the efficiency of the proposed method gradually increases and tends to be stable. In addition, the complexity for solving problem is extremely reduced.