Differentiation of syndromes with SVM

Authors:
Zhanquan Sun;Guangcheng Xi;Jianqiang Yi
Affiliations:
Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Academy of Sciences, Beijing, China;Key Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Academy of Sciences, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 5
Cited 0

Original Contribution: Least mean square error reconstruction principle for self-organizing neural-nets

Neural Networks
Representation and separation of signals using nonlinear PCA type learning

Neural Networks
Support-Vector Networks

Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
A coordination method for fuzzy multi-objective optimization of system reliability

Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Differentiation of syndromes is the kernel theory of Traditional Chinese Medicine (TCM). How to diagnose syndromes correctly with scientific means according to symptoms is the first problem in TCM. Several modern approaches have been applied, but no satisfied results have been obtained because of the complexity of diagnosis procedure. Support Vector Machine (SVM) is a new classification technique and has drawn much attention on this topic in recent years. In this paper, we combine non-linear Principle Component Analysis (PCA) neural network with multi-class SVM to realize differentiation of syndromes. Non-linear PCA is used to preprocess clinical data to save computational cost and reduce noise. The multi-class SVM takes the non-linear principle components as its inputs and determines a corresponding syndrome. Analyzing of a TCM example shows its effectiveness.