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Proceedings Paper

Comparative evaluation of pattern recognition algorithms: statistical, neural, fuzzy, and neuro-fuzzy techniques
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Paper Abstract

Pattern recognition by fuzzy, neural, and neuro-fuzzy approaches, has gained popularity partly because of intelligent decision processes involved in some of the above techniques, thus providing better classification and partly because of simplicity in computation required by these methods as opposed to traditional statistical approaches for complex data structures. However, the accuracy of pattern classification by various methods is often not considered. This paper considers the performance of major fuzzy, neural, and neuro-fuzzy pattern recognition algorithms and compares their performances with common statistical methods for the same data sets. For the specific data sets chosen namely the Iris data set, an the small Soybean data set, two neuro-fuzzy algorithms, AFLC and IAFC, outperform other well- known fuzzy, neural, and neuro-fuzzy algorithms in minimizing the classification error and equal the performance of the Bayesian classification. AFLC, and IAFC also demonstrate excellent learning vector quantization capability in generating optimal code books for coding and decoding of large color images at very low bit rates with exceptionally high visual fidelity.

Paper Details

Date Published: 13 October 1998
PDF: 12 pages
Proc. SPIE 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, (13 October 1998); doi: 10.1117/12.326718
Show Author Affiliations
Sunanda Mitra, Texas Tech Univ. (United States)
Ramiro Castellanos, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 3455:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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