Evolutionary Instance Selection Algorithm based on Takagi-Sugeno Fuzzy Model
- Authors
- Lee, Sang-Hong; Lim, Joon S.
- Issue Date
- May-2014
- Publisher
- NATURAL SCIENCES PUBLISHING CORP-NSP
- Keywords
- Instance selection; feature selection; Takagi-Sugeno fuzzy model; McNemar' s test; normal distribution
- Citation
- APPLIED MATHEMATICS & INFORMATION SCIENCES, v.8, no.3, pp.1307 - 1312
- Journal Title
- APPLIED MATHEMATICS & INFORMATION SCIENCES
- Volume
- 8
- Number
- 3
- Start Page
- 1307
- End Page
- 1312
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12636
- DOI
- 10.12785/amis/080346
- ISSN
- 2325-0399
- Abstract
- In this study, we propose evolutionary instance selection based on the Takagi-Sugeno (T-S) fuzzy model. The previous neural network with weighted fuzzy membership functions (NEWFM) supports feature selection; thus, it enables the selection of minimum features with the highest performance. The enhanced NEWFM supports a weighted mean defuzzification in the T-S fuzzy model with a confidence interval in the normal distribution; thus, it enables the selection of minimum instances with the highest performance. The enhanced NEWFM has two stages; feature selection is performed in the first stage, whereas instance selection is performed in the second stage. The performance of the enhanced NEWFM is compared with that of the previous NEWFM. In addition, McNemar's test reveals a significant difference between the performances of both NEWFMs (p < 0.05).
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - IT융합대학 > 컴퓨터공학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/12636)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.