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Fuzzy inference system for classification with dimension reduction of rough set

Author(s)
SUGIYARTO SURONO Department of Mathematics, Ahmad Dahlan University, Yogyakarta, Indonesia
CHOO WOU ONN Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
ZANI ANJANI RAFSANJANI Master in Mathematics Education, Ahmad Dahlan University, Yogyakarta, Indonesia
ANNISA EKA HARYATI Master in Mathematics Education, Ahmad Dahlan University, Yogyakarta, Indonesia
GOH KHANG WEN Faculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia
Abstract
Data obtained from various measurements will have different characteristics. Different data sets can be classified according to the characteristics of each data. One of the classification methods is the Takagi Sugeno Kang (TSK) fuzzy inference system, where the fuzzy TSK output is a constant, linear or polynomial. However, one of the obstacles in fuzzy TSK is regarding the dimensions of the data. Therefore, we propose a dimension reduction using the rough set in this study. Then, the results of the rough set will be used as input to the fuzzy TSK, and each rule will be optimized using MBGD and SGD. This study compares TSK's accuracy and computational time results using MBGD and SGD. The results indicate that the average time of the MBGD-UR produces the shortest time. In addition, MBGD-UR has a time that tends to be more stable than other methods. Then, the BCA value shows that MBGD-A has the most significant BCA value. Therefore, MBGD-A has the best performance compared to other methods.
Publication Details
Page(s) 6741-6757
DOI DOI not available
Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 22, Year: 2022

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Keywords
Dimension reduction, Fuzzy TSK, Rough set., SG, MBGD
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