[WITHDRAWN] Boosting Relevance Vector Machine Learning Algorithm Based on Noise Self-Detection

DOI

10.2991/mecae-18.2018.29How to use a DOI?

Keywords

Relevant vector machines; AdaBoost; Noise detection.

Abstract

AdaBoost is an ensemble method to construct a strong classifier with linear combination of base classifiers, which has been applied to relevance vector machine (RVM) for performance improvement. However, the combination of the RVM and AdaBoost can be overfitting in dealing with the noisy data sets because of the inherent noise sensitivity of AdaBoost. Therefore, a boosting relevance vector machine learning algorithm based on noise self-detection is proposed, which can detect the sample type on top of the posterior probability output of RVM, remove the noisy samples and give more emphasis on the boundary samples to generate the classifiers. The algorithm was applied to real data sets, and experimental results show that the proposed method offers good performance on accuracy and generalization.

Copyright

© 2018, 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  - Wangchen Qin
AU  - Fang Liu
AU  - Quan Qi
AU  - Mi Tong
PY  - 2018/03
DA  - 2018/03
TI  - [WITHDRAWN] Boosting Relevance Vector Machine Learning Algorithm Based on Noise Self-Detection
BT  - Proceedings of the 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018)
PB  - Atlantis Press
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-18.2018.29
DO  - 10.2991/mecae-18.2018.29
ID  - Qin2018/03
ER  -