Comparative evaluation of pattern recognition algorithms: Statistical, neural, fuzzy, and neuro-fuzzy techniques

Sunanda Mitra, Ramiro Castellanos

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)248-259
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 1998
EventApplications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation - San Diego, CA, United States
Duration: Jul 20 1998Jul 20 1998


  • Clustering
  • Fuzzy membership
  • Neuro-fuzzy clustering
  • Pattern recognition
  • Self-Organizing Neural Networks


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