Multi-class learning with specific features for pairwise classes

Yan, Jianjun; Shen, Qing; Zhou, Chiheng; Ren, Jintao; Guo, Rui

doi:10.1109/bmei.2011.6098691

Cited by 2 publications

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A multiclass SVM classifier is used to identify the auscultation signals. Each SVM classifier chooses specific RBF kernel parameters through a grid search with a nested cross-validation [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine

Yan

Guo

Wang

et al. 2014

Evidence-Based Complementary and Alternative Medicine

Self Cite

View full text Add to dashboard Cite

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

show abstract

Section: Methodsmentioning

confidence: 99%