Construct Offline and Online Membership Functions Based on SVM for Classification

DOI

10.2991/iske.2007.61How to use a DOI?

Keywords

Online membership function, Offline membership function, SVM, Tuing-scaled SVC

Abstract

The classification algorithm presented in this paper consists of Offline and Online Membership Functions, named as OOMF. They cooperated with each other to provide qualified class label of confidence. The offline membership function is derived from decision functions yielded by a weighted SVMs approach (WSVM). The online membership function works in the scenario where offline membership function is of low discrimination. And it is designed by a new kNN (NkNN) that is encoded with a class-wise metric. Some strategies bring computational ease: hyper parameters concerned are tuned context-dependently; training dataset is reduced by a tuning support vector clustering (TSVC); and working set of NkNN is pre-specified. We describe experimental evidence of classification performance improved by our schema over state of the arts on real datasets.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiangsheng Rong
AU  - Ping Ling
PY  - 2007/10
DA  - 2007/10
TI  - Construct Offline and Online Membership Functions Based on SVM for Classification
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 364
EP  - 368
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.61
DO  - 10.2991/iske.2007.61
ID  - Rong2007/10
ER  -