Three-way Cognitive Concept Learning Via Information Fusion

The theory of three-way decisions is to consider a decision-making problem as a ternary classification one which is realized by the acceptance,rejection and non-commitment.Recently,this theory has been integrated with formal concept analysis in two different ways:constructive and axiomatic methods.The constructive method is to define certain three-way concepts in a formal context to support three-way concept learning.Nevertheless,there are similarities between the constructive and axiomatic methods,both of them can be realized by incorporating the idea of ternary classification into the design of extension or intension of a concept.However,their information fusion abilities need to be improved since neither of them is able to deal with large or multi-source data.Motivated by this problem,our paper is to reconsider cognition based three-way concept learning from the perspective of information fusion.That is,the parallel computing techniques of learning three-way concepts are developed for large and multi-source data.Specifically,for large data,the relationship between the global granular concept and the local ones is first clarified,and then it is used to induce three-way decisions which is established by aggregating each single-source data,and three-way concept learning is made by constructing lower and upper approximation concepts.Finally,we conduct some numerical experiments to evaluate the effectiveness of the proposed parallel computing algorithms.