Although the great progress in sensor and signal processing techniques have provided effective tools for quantitative research into traditional Chinese pulse diagnosis, the automatic classification of pulse waveform is remained a difficult problem. In order to address this issue, we propose a novel edit distance with real penalty-based k-nearest neighbor classifier by referring to recent progress in time series matching and KNN classifier. Taking advantage of the metric property of ERP, we develop an ERP-induced inner product operator and then embed it into difference-weighted KNN classifier. Experimental results show that the proposed classifier is more accurate than comparable pulse waveform classification approaches. © 2010 Springer-Verlag.
CITATION STYLE
Zhang, D., Zuo, W., Li, Y., & Li, N. (2010). Pulse waveform classification using ERP-based difference-weighted KNN classifier. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6165 LNCS, pp. 191–200). https://doi.org/10.1007/978-3-642-13923-9_20
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