Concept Hierarchies Generation for Classification using Fuzzy Formal Concept Analysis

A large collection of formal concepts can be a hedge of the application of Formal Concept Analysis and is not directly comprehensible for a user. It is thus an important task to develop methods which help to overcome the problem of large number of extracted formal concepts. This paper proposes concept hierarchies learning for getting more concise concept representation method using fuzzy formal concept analysis. Then, concept hierarchies based classifier is produced. At the end, experiments show that the compression rate of concept hierarchy to concept lattice is obvious while preserving the accuracy of classification.