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Instance-Based Learning Algorithms
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Fuzzy Sets and Systems
Further remarks on the relation between rough and fuzzy sets
Fuzzy Sets and Systems
Voting over Multiple Condensed Nearest Neighbors
Artificial Intelligence Review - Special issue on lazy learning
Self-organizing maps
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Information Sciences: an International Journal
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Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Artificial Intelligence Review - Special issue on lazy learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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Pattern Recognition Letters
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Fuzzy Sets and Systems
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IEEE Transactions on Computers
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
Weighted k-nearest leader classifier for large data sets
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
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IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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IEEE Transactions on Information Theory
The condensed nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
The reduced nearest neighbor rule (Corresp.)
IEEE Transactions on Information Theory
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IEEE Transactions on Information Theory
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PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
A novel possibilistic fuzzy leader clustering algorithm
International Journal of Hybrid Intelligent Systems - Rough and Fuzzy Methods for Data Mining
Hubness-based fuzzy measures for high-dimensional k-nearest neighbor classification
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
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A leaders set which is derived using the leaders clustering method can be used in place of a large training set to reduce the computational burden of a classifier. Recently, a fast and efficient leader-based classifier called weighted k-nearest leader-based classifier is shown by us to be an efficient and faster classifier. But, there exist some uncertainty while calculating the relative importance (weight) of the prototypes. This paper proposes a generalization over the earlier proposed k-nearest leader-based classifier where a novel soft computing approach is used to resolve the uncertainty. Combined principles of rough set theory and fuzzy set theory are used to analyze the proposed method. The proposed method called rough-fuzzy weighted k-nearest leader classifier (RF-wk-NLC) uses a two level hierarchy of prototypes along with their relative importance. RF-wk-NLC is shown by using some standard data sets to have improved performance and is compared with the earlier related methods.