Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
A Rough Set Approach to Measuring Information Granules
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
Granulation and nearest neighborhoods: rough set approach
Granular computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Case Generation Using Rough Sets with Fuzzy Representation
IEEE Transactions on Knowledge and Data Engineering
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Is there a need for fuzzy logic?
Information Sciences: an International Journal
Mixed feature selection based on granulation and approximation
Knowledge-Based Systems
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Fuzzy Aggregation with Artificial Color filters
Information Sciences: an International Journal
Toward a generalized theory of uncertainty (GTU)--an outline
Information Sciences: an International Journal
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rough fuzzy MLP: knowledge encoding and classification
IEEE Transactions on Neural Networks
Multilayer perceptron, fuzzy sets, and classification
IEEE Transactions on Neural Networks
Granular Computing Models in the Classification of Web Content Data
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
Explicit rough-fuzzy pattern classification model
Pattern Recognition Letters
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A new rough-fuzzy model for pattern classification based on granular computing is described in the present article. In this model, we propose the formulation of class-dependent granules in fuzzy environment. Fuzzy membership functions are used to represent the feature-wise belonging to different classes, thereby producing fuzzy granulation of the feature space. The fuzzy granules thus generated possess better class discriminatory information that is useful in pattern classification with overlapping classes. Neighborhood rough sets are used in the selection of a subset of granulated features that explore the local/contextual information from neighbor granules. The model thus explores mutually the advantages of class-dependent fuzzy granulation and neighborhood rough set. The superiority of the proposed model to other similar methods is established with seven completely labeled data sets, including a synthetic remote sensing image, and two partially labeled real remote sensing images collected from satellites. Various performance measures, including a new method of dispersion estimation, are used for comparative analysis. The new measure called ''dispersion score'' quantifies the nature of distribution of the classified patterns among different classes so that lower is the dispersion, better is the classifier. The proposed model learns well even with a lower percentage of training set that makes the system fast. The model is seen to have lowest dispersion measure (i.e., misclassified patterns are confined to minimum number of classes) compared to others; thereby reflecting well the overlapping characteristics of a class with others, and providing a strong clue for the class-wise performance improvement with available higher-level information. The statistical significance of the proposed model is also supported by the @g^2 test.