Abstract
Deep learning, which has the ability to extract significant patterns from dataset, becomes increasingly important with regard to the information age that has the characteristics of diversity and large volume of data. Compared with deep neural networks (DNN), support vector machine (SVM) is one of the most representative models of shallow neural networks (SNN), and has been widely used in various fields since it has the ability to process high-dimensional dataset effectively. Furthermore, by considering the prior knowledge of monotonicity, SVM can enhance its generalization capability against noise data. Monotonicity-constrained SVM (MC-SVM) is a model that take prior knowledge of monotonicity into account and this model is proven to outperform conventional SVM. To benefit from the advantage of deep neural network on feature extraction and performance of MC-SVM, we propose a deep learning model called deep MC-SVM (DMC-SVM) that takes MC-SVM as feature extractor to improve the generalization property of the classifier, especially when solving complicated classification problem. We took two real-world datasets, Wisconsin Breast Cancer Diagnostic (WBCD) and PD600 as our training datasets, and the experiment results demonstrated that DMC-SVM could extract more abstract features that make this model more generalized, and for this reason, DMC-SVM has the better ability while dealing with classification problems.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 530-535 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538674475 |
| DOIs | |
| Publication status | Published - 2019 Apr 16 |
| Event | 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 - Yonago, Japan Duration: 2018 Jul 8 → 2018 Jul 13 |
Publication series
| Name | Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 |
|---|
Conference
| Conference | 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018 |
|---|---|
| Country | Japan |
| City | Yonago |
| Period | 18-07-08 → 18-07-13 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Communication
- Information Systems
- Information Systems and Management
- Education
Cite this
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Deep Learning for Monotonic Support Vector Machines. / Lo, Ming Chin; Tsai, Bing Han; Li, Sheng-Tun.
Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 530-535 8693415 (Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Deep Learning for Monotonic Support Vector Machines
AU - Lo, Ming Chin
AU - Tsai, Bing Han
AU - Li, Sheng-Tun
PY - 2019/4/16
Y1 - 2019/4/16
N2 - Deep learning, which has the ability to extract significant patterns from dataset, becomes increasingly important with regard to the information age that has the characteristics of diversity and large volume of data. Compared with deep neural networks (DNN), support vector machine (SVM) is one of the most representative models of shallow neural networks (SNN), and has been widely used in various fields since it has the ability to process high-dimensional dataset effectively. Furthermore, by considering the prior knowledge of monotonicity, SVM can enhance its generalization capability against noise data. Monotonicity-constrained SVM (MC-SVM) is a model that take prior knowledge of monotonicity into account and this model is proven to outperform conventional SVM. To benefit from the advantage of deep neural network on feature extraction and performance of MC-SVM, we propose a deep learning model called deep MC-SVM (DMC-SVM) that takes MC-SVM as feature extractor to improve the generalization property of the classifier, especially when solving complicated classification problem. We took two real-world datasets, Wisconsin Breast Cancer Diagnostic (WBCD) and PD600 as our training datasets, and the experiment results demonstrated that DMC-SVM could extract more abstract features that make this model more generalized, and for this reason, DMC-SVM has the better ability while dealing with classification problems.
AB - Deep learning, which has the ability to extract significant patterns from dataset, becomes increasingly important with regard to the information age that has the characteristics of diversity and large volume of data. Compared with deep neural networks (DNN), support vector machine (SVM) is one of the most representative models of shallow neural networks (SNN), and has been widely used in various fields since it has the ability to process high-dimensional dataset effectively. Furthermore, by considering the prior knowledge of monotonicity, SVM can enhance its generalization capability against noise data. Monotonicity-constrained SVM (MC-SVM) is a model that take prior knowledge of monotonicity into account and this model is proven to outperform conventional SVM. To benefit from the advantage of deep neural network on feature extraction and performance of MC-SVM, we propose a deep learning model called deep MC-SVM (DMC-SVM) that takes MC-SVM as feature extractor to improve the generalization property of the classifier, especially when solving complicated classification problem. We took two real-world datasets, Wisconsin Breast Cancer Diagnostic (WBCD) and PD600 as our training datasets, and the experiment results demonstrated that DMC-SVM could extract more abstract features that make this model more generalized, and for this reason, DMC-SVM has the better ability while dealing with classification problems.
UR - http://www.scopus.com/inward/record.url?scp=85065200189&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065200189&partnerID=8YFLogxK
U2 - 10.1109/IIAI-AAI.2018.00114
DO - 10.1109/IIAI-AAI.2018.00114
M3 - Conference contribution
T3 - Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
SP - 530
EP - 535
BT - Proceedings - 2018 7th International Congress on Advanced Applied Informatics, IIAI-AAI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -