Neural Networks
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Haar Wavelets for Efficient Similarity Search of Time-Series: With and Without Time Warping
IEEE Transactions on Knowledge and Data Engineering
Blind feature extraction for time-series classification using haar wavelet transform
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Classification of multivariate time series using two-dimensional singular value decomposition
Knowledge-Based Systems
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Many feature extraction algorithms have been proposed for time series classification. However, most of the proposed algorithms in time series data mining community belong to the unsupervised approach, without considering the class separability capability of features that is important for classification. In this paper we propose a supervised feature extraction approach by selecting discriminating wavelet coefficients to improve the time series classification accuracy. After wavelet transformation, few wavelet coefficients with higher class separability capability are selected as features. We apply three feature evaluation criteria, i.e., Fisher’s discriminant ratio, divergence, and Bhattacharyya distance. Experiments performed on several benchmark time series datasets demonstrate the effectiveness of the proposed approach.