NDLI: Computer aided test and diagnosis model for exceptional signals detection and classification by using WT-SVM

Content Provider	IEEE Xplore Digital Library
Author	Guang-Ming Xian Zhi-Yan Wang Guang-Lin Xian
Copyright Year	2005
Description	Author affiliation: Sch. of Comput. Sci. & Eng., South China Univ. of Technol., Guangzhou, China (Guang-Ming Xian; Zhi-Yan Wang; Guang-Lin Xian)
Abstract	In order to detect and diagnosis the exceptional signals, this paper presents a new computer aided test and diagnosis (CAT/CAD) model based on wavelet transform (WT) and support vector machine (SVM). The architecture of the CAT and CAD model and experimental feature extraction and pattern recognition by WT-SVM system are presented. The features of special frequency segment of the signal picked up by the method of wavelet decomposition are used as the inputs of SVM. The analysis of the experimental data proves that the model proposed is efficient and simple to recognize exceptional signals. The experimental result indicated that the error rate of SVM is lower than the one obtained by applying KNN classifier and Fisher classifier methods whether the training set is small or huge. The experiment also demonstrates that SVM can be extremely effective in minimizing the error rate lower than LVQ and BP neural network methods.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	4271
Ending Page	4276
File Size	207304
Page Count	6
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527687
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Testing Signal detection Support vector machines Support vector machine classification Error analysis Wavelet transforms Feature extraction Pattern recognition Frequency Signal analysis machine learning Computer aided test and diagnosis detection and classification wavelet transform support vector machine statistic learning theory signal recognition
Content Type	Text
Resource Type	Article

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