Published Oct 29, 2012


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Andrea Villate-Gil, BSc

David Eduardo Rincon-Arandia, BSc

Miguel Alberto Melgarejo-Rey, MSc



This paper presents a methodologicalapproach for tuning fuzzy classifiersintended to recognize the Australiansign-language considering twoparticular contexts. We describe thefuzzy classification architecture andthe tuning process based on differentialevolution. The validation resultsshow that it is possible to find a fuzzyclassifier whose classification error isaround 13.0% over a group of wordstaken from several experts for eachinteraction context. This characteristicis relevant as previous works only consideredrecognizing words providedonly by one interpreter.


Auslan, differential evolution, fuzzy classification, pattern recognition, sign language, optimization, TSK Fuzzy SystemsAuslan, clasificación difusa, evolución diferencial, lenguaje de señas, reconocimiento de patrones, optimización, sistemas difusos TSK

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How to Cite
Villate-Gil, A., Rincon-Arandia, D. E., & Melgarejo-Rey, M. A. (2012). Applying differential evolution to tune fuzzy classifiers intended for sign-language recognition. Ingenieria Y Universidad, 16(2), 397.

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