A new fuzzy approach for multi-source decision fusion

Nowadays, we are facing the rapidly growing amount of data being produced in many organizations, social networks and internet. These data are generated in disparate locations and their aggregation into one location is exceedingly time and space consuming. Traditional statistical methods are not sufficient for processing of this massive multi-source data. In this paper, we propose a new fuzzy-based decision fusion approach for classification problems of this kind. The necessity of fuzzy information arises in distributed classification because imprecision, uncertainty and ambiguity can be found at all information sources, from the data itself to the results of the classifiers. In the proposed approach, multiple classifiers are constructed based on different information sources which have different degrees of reliability. Then a fuzzy rule based system is designed for approximating distribution of reliabilities of sources over the input space. The decision fusion of multiple classifiers takes place using the estimated degrees of sources' reliabilities. Comparison results are made between both centralized classification and two other distributed classification methods. One is averaging and the other is discounting each classifier's decision based on its accuracy. Results show the high accuracy of the proposed method in making decisions in distributed environments, without the overhead of aggregating the entire data in one location.