Constraint selection on monotonic support vector classifiers

Chih Chuan Chen, Hui Chi Chuang, Yi Chung Cheng, Sheng Tun Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Support Vector Machines (SVMs) are widely used data mining techniques owing to their excellent abilities in solving both classification and regression problems. Various efforts have been dedicated to improve SVMs based on different scenarios of the real world problems. It has been observed that some monotonic relationships exist between attributes and the output values, and SVMs that consider such prior knowledge in some application fields obtain certain advantages. Previous studies showed that SVMs incorporating monotonicity constraints, so-called MC-SVM, could improve the performance of SVMs in some applications. In this research, we aim to explore the impacts on predictive performance of MC-SVM model with respect to constructing methods as well as different number of monotonicity constraints.

Original languageEnglish
Title of host publicationProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
EditorsAyako Hiramatsu, Tokuro Matsuo, Akimitsu Kanzaki, Norihisa Komoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages641-646
Number of pages6
ISBN (Electronic)9781467389853
DOIs
Publication statusPublished - 2016 Aug 31
Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
Duration: 2016 Jul 102016 Jul 14

Publication series

NameProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
CountryJapan
CityKumamoto
Period16-07-1016-07-14

Fingerprint

Support vector machines
Classifiers
Data mining

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Chen, C. C., Chuang, H. C., Cheng, Y. C., & Li, S. T. (2016). Constraint selection on monotonic support vector classifiers. In A. Hiramatsu, T. Matsuo, A. Kanzaki, & N. Komoda (Eds.), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 641-646). [7557691] (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.173
Chen, Chih Chuan ; Chuang, Hui Chi ; Cheng, Yi Chung ; Li, Sheng Tun. / Constraint selection on monotonic support vector classifiers. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. editor / Ayako Hiramatsu ; Tokuro Matsuo ; Akimitsu Kanzaki ; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 641-646 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).
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abstract = "Support Vector Machines (SVMs) are widely used data mining techniques owing to their excellent abilities in solving both classification and regression problems. Various efforts have been dedicated to improve SVMs based on different scenarios of the real world problems. It has been observed that some monotonic relationships exist between attributes and the output values, and SVMs that consider such prior knowledge in some application fields obtain certain advantages. Previous studies showed that SVMs incorporating monotonicity constraints, so-called MC-SVM, could improve the performance of SVMs in some applications. In this research, we aim to explore the impacts on predictive performance of MC-SVM model with respect to constructing methods as well as different number of monotonicity constraints.",
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Chen, CC, Chuang, HC, Cheng, YC & Li, ST 2016, Constraint selection on monotonic support vector classifiers. in A Hiramatsu, T Matsuo, A Kanzaki & N Komoda (eds), Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557691, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Institute of Electrical and Electronics Engineers Inc., pp. 641-646, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16-07-10. https://doi.org/10.1109/IIAI-AAI.2016.173

Constraint selection on monotonic support vector classifiers. / Chen, Chih Chuan; Chuang, Hui Chi; Cheng, Yi Chung; Li, Sheng Tun.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. ed. / Ayako Hiramatsu; Tokuro Matsuo; Akimitsu Kanzaki; Norihisa Komoda. Institute of Electrical and Electronics Engineers Inc., 2016. p. 641-646 7557691 (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Chen CC, Chuang HC, Cheng YC, Li ST. Constraint selection on monotonic support vector classifiers. In Hiramatsu A, Matsuo T, Kanzaki A, Komoda N, editors, Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 641-646. 7557691. (Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016). https://doi.org/10.1109/IIAI-AAI.2016.173