A fuzzy comprehensive evaluation method based on the joint distribution dynamic membership degree for classification problems

Abstract

In this study, we propose a new classification method by adopting some ideas originating from the fuzzy comprehensive evaluation (FCE). To make the FCE be a classifier, the class labels in classification problems are regarded as the evaluation remarks in the FCE, and the attributes in these two domains are regarded to be consistent. Then, to implement the FCE model B = W ∘ R and obtain an accurate classification result, on the one hand, a learning algorithm, which is based on the joint distribution of attribute values and is dynamic, is proposed to construct the fuzzy relational matrix R; on the other hand, equal weight is considered to constitute the weight vector W. Meanwhile, for a continuous dataset, the discretization method and the determination of the discretization class number corresponding to the proposed classifier are discussed. The proposed classifier not only innovatively extends the FCE to data mining but also has its own classification advantages, that is, it is easy to operate and has good interpretability. Finally, we perform some numerical experiments using publicly available datasets, and the experimental results demonstrate that the proposed classifier outperforms some existing classifiers.