Classification of lip color based on multiple SVM-RFE
- Resource Type
- Conference
- Authors
- Wang, Jingjing; Li, Xiaoqiang; Fan, Huafu; Li, Fufeng
- Source
- 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on. :769-772 Nov, 2011
- Subject
- Computing and Processing
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Image color analysis
Feature extraction
Histograms
Support vector machines
Skin
Medical diagnostic imaging
Multiple SVM-RFE
lip color classification
feature selection
- Language
Classification of lip color is an important aspect in the theory of Traditional Chinese Medicine (TCM). The lip color of one person can reflect the person's healthy status. This paper investigates the effectiveness of multiple support vector machine recursive feature elimination (SVM-RFE) for feature selection in the classification of lip color. In the proposed method, both the normalized histogram features and the mean/variance features are computed for the ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. Experimental results show that not only the multiple SVM-RFE is effective for feature selection in the lip color classification, but also the accuracy rate of classification of the proposed method is better than the existing SVM method, which is close up to 91%.