Fuzzy lattice reasoning for pattern classification using a new positive valuation function

Authors:
Yazdan Jamshidi Khezeli;Hossein Nezamabadi-pour
Affiliations:
Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran;Department of Electrical Engineering, Shahid Bahonar University of Kerman, Kerman, Iran
Venue:
Advances in Fuzzy Systems
Year:
2012

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Abstract

This paper describes an enhancement of fuzzy lattice reasoning (FLR) classifier for pattern classification based on a positive valuation function. Fuzzy lattice reasoning (FLR) was described lately as a lattice data domain extension of fuzzy ARTMAP neural classifier based on a lattice inclusion measure function. In this work, we improve the performance of FLR classifier by defining a new nonlinear positive valuation function. As a consequence, the modified algorithm achieves better classification results. The effectiveness of the modified FLR is demonstrated by examples on several well-known pattern recognition benchmarks.