Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Information-preserving hybrid data reduction based on fuzzy-rough techniques
Pattern Recognition Letters
A novel fuzzy classifier based on product aggregation operator
Pattern Recognition
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
On the compact computational domain of fuzzy-rough sets
Pattern Recognition Letters
Pattern Recognition Letters
Rough set based approaches to feature selection for Case-Based Reasoning classifiers
Pattern Recognition Letters
Fuzzy Classifier Design
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
Fuzzy probabilistic approximation spaces and their information measures
IEEE Transactions on Fuzzy Systems
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
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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.