[WITHDRAWN] Boosting Relevance Vector Machine Learning Algorithm Based on Noise Self-Detection
Wangchen Qin, Fang Liu, Quan Qi, Mi Tong
Available Online March 2018.
- https://doi.org/10.2991/mecae-18.2018.29How to use a DOI?
- Relevant vector machines; AdaBoost; Noise detection.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wangchen Qin AU - Fang Liu AU - Quan Qi AU - Mi Tong PY - 2018/03 DA - 2018/03 TI - [WITHDRAWN] Boosting Relevance Vector Machine Learning Algorithm Based on Noise Self-Detection BT - 2018 International Conference on Mechanical, Electronic, Control and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 - https://doi.org/10.2991/mecae-18.2018.29 ID - Qin2018/03 ER -