Binary Classification of Multigranulation Searching Algorithm Based on Probabilistic Decision

Qinghua Zhang,Tao Zhang

doi:10.1155/2016/9329812

Abstract

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.

Highlights

With the rapid development of modern science and technology, the daily information which people are facing is dramatically increasing, and it is urgent to find a simple and effective way to process the complex information
We mainly focus on establishing a minimum granulation expectation model of classification times by multigranulation computing method
In the process of knowledge cognition, granulating a huge problem into lots of small subproblems means to simplify the original complex problem and deal with these subproblems in different granularity spaces [64]. This hierarchical computing model is very effective for getting a complete solution or approximate solution of the original problem due to its idea of divide and conquer. Many scholars pay their attention to efficient searching algorithms based on granular computing theory

Summary

Introduction

With the rapid development of modern science and technology, the daily information which people are facing is dramatically increasing, and it is urgent to find a simple and effective way to process the complex information. In 1986, Professor Mingmin and Junli proposed that using the single-level group testing method can reduce the workloads of massive blood analysis when the prevalence rate p of a sickness is less than about 0.3 [43]. In this method, all objects will be subdivided into many small subgroups, and every subgroup will be tested. A binary classification of multilevels granulation searching algorithm, namely, establishing an efficient multigranulation binary classification searching model based on hierarchical quotient space structure, is proposed in this paper.

Preliminary

Result

Comparative Analysis on Experimental Results

Findings

Conclusions