Paper Info
Title
Encoding Words Into Interval Type-2 Fuzzy Sets Using an Interval Approach
Abstract
This paper presents a very practical type-2-fuzzistics methodology for obtaining interval type-2 fuzzy set (IT2 FS) models for words, one that is called an interval approach (IA). The basic idea of the IA is to collect interval endpoint data for a word from a group of subjects, map each subject's data interval into a prespecified type-1 (T1) person membership function, interpret the latter as an embedded T1 FS of an IT2 FS, and obtain a mathematical model for the footprint of uncertainty (FOU) for the word from these T1 FSs. The IA consists of two parts: the data part and the FS part. In the data part, the interval endpoint data are preprocessed, after which data statistics are computed for the surviving data intervals. In the FS part, the data are used to decide whether the word should be modeled as an interior, left-shoulder, or right-shoulder FOU. Then, the parameters of the respective embedded T1 MFs are determined using the data statistics and uncertainty measures for the T1 FS models. The derived T1 MFs are aggregated using union leading to an FOU for a word, and finally, a mathematical model is obtained for the FOU. In order that all researchers can either duplicate our results or use them in their research, the raw data used for our codebook examples, as well as a MATLAB M-file for the IA, have been put on the Internet at: http://sipi.usc.edu/ ~ mendel.
Year
DOI
Venue
2008
10.1109/TFUZZ.2008.2005002
IEEE T. Fuzzy Systems
Keywords
Field
DocType
encoding words,fs part,fuzzistics,perceptual computer (per-c),fuzzy set theory,data interval,data statistic,interval type-2 fuzzy sets (it2 fs),t1 mfs,raw data,word processing,interval type-2 fuzzy sets,mathematical model,interval endpoint data,t1 fs,encoder,computing with words,interval approach (ia),data part,natural language processing,interval type-2 fuzzy,it2 fs,interval approach,fuzzy sets,encoding,statistics,measurement uncertainty,decoding,engines,internet,membership function
Perceptual computing,Measurement uncertainty,Algorithm,Fuzzy set,Artificial intelligence,Encoder,Membership function,Machine learning,Word processing,Mathematics,Codebook,Encoding (memory)
Journal
Volume
Issue
ISSN
16
6
1063-6706
Citations 
PageRank 
References 
67
1.97
15
Authors
2
Name
Order
Citations
PageRank
Feilong Liu142915.52
Mendel, J.M.2109261042.42