The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-398

“Interval Type-2 Fuzzy Set Subsethood Measures as a Decoder for Perceptual Computing”

by Dongrui Wu and Jerry M. Mendel

December 2009

In some applications of computing with words, it is necessary to map an interval type-2 fuzzy set (IT2 FS) into one of several classes, which are also represented by IT2 FSs. This classifier can be implemented by a subsethood measure. Five existing subsethood measures for IT2 FSs are considered in this paper. Comparative studies show that Vlachos and Sergiadis's IT2 FS subsethood measure gives the most reasonable outputs as a decoder in computing with words when the desired output is a class. The results in this paper will be useful in constructing a third kind of decoder (i.e., in addition to similarity measures and ranking methods) for perceptual computing.

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