Explicit rough–fuzzy pattern classification model

• The paper develops a rough-fuzzy pattern classification model, integrating the merits of both fuzzy and rough sets.
• Neighborhood rough sets are used for feature selection.
• Demonstrates the potentiality of the model on both completely and partially labelled data sets including remote sensing images.

An explicit rough–fuzzy model for pattern classification is proposed in the present paper. The model explores and provides the synergistic integration of the merits of both fuzzy and rough sets. It acquires improved learning and generalization capabilities through explicit fuzzification of input features. Likely optimal features are selected from these fuzzified features using neighborhood rough sets, which utilize the neighborhood relative information. The combined class belonging information of features in the designing process of model further enhances its decision-making ability. The resultant features thus provide comprehensive framework for building discriminative pattern classification models for the data sets with highly overlapping class boundaries. The efficacy of the proposed model is verified with four completely labeled data sets including one synthetic remote sensing image, and one partially labeled real remote sensing image. Various performance measurement indexes supported the superiority claim of the model.