Feature selection by a distance measure method of subnormal and non-convex fuzzy sets
- Authors
- Qu, Letao; Wang, Bohyun; Lim, Joon S.
- Issue Date
- Nov-2021
- Publisher
- IOS PRESS
- Keywords
- bounded sum; distance measures; Embedded feature selection; fuzzy neural networks; non-covex fuzzy sets; sub-normal fuzzy sets
- Citation
- JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, v.41, no.4, pp.5199 - 5205
- Journal Title
- JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
- Volume
- 41
- Number
- 4
- Start Page
- 5199
- End Page
- 5205
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/82718
- DOI
- 10.3233/JIFS-219005
- ISSN
- 1064-1246
- Abstract
- Distance measures of fuzzy sets have been developed for feature selection and finding redundant features in the fields of decision-making, prediction, and classification problems. Terms commonly used in the definition of fuzzy sets are normal and convex fuzzy sets. This paper extends the general fuzzy set definitions to subnormal and non-convex fuzzy sets that are more precise when implementing uncertain knowledge representations by weighing fuzzy membership functions. A distance measure method for subnormal and non-convex fuzzy sets is proposed for embedded feature selection. Constructing fuzzy membership functions and extracting fuzzy rules play a critical role in fuzzy classification systems. The weighted fuzzy membership functions prevent the combinatorial explosion of fuzzy rules in multiple fuzzy rule-based systems. The proposed method was validated by a comparison with two other methods. Our proposed method demonstrated higher accuracies in training and test, with scores of 97.95% and 93.98%, respectively, compared to the other two methods. © 2021 - IOS Press.
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Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
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