Applying differential evolution to tune fuzzy classifiers intended for sign-language recognition
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Keywords

Auslan
differential evolution
fuzzy classification
pattern recognition
sign language
optimization
TSK Fuzzy Systems

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Applying differential evolution to tune fuzzy classifiers intended for sign-language recognition. (2012). Ingenieria Y Universidad, 16(2), 397. https://doi.org/10.11144/Javeriana.iyu16-2.adet
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Abstract

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.

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