Applying Prototype Selection and Abstraction Algorithms for Efficient Time-Series Classification
A widely used time series classification method is the single nearest neighbour. It has been adopted in many time series classification systems because of its simplicity and effectiveness. However, the efficiency of the classification process depends on the size of the training set as well as on data dimensionality. Although many speed-up methods for fast time series classification have been proposed and are available in the literature, state-of-the-art, non-parametric prototype selection and abstraction data reduction techniques have not been exploited on time series data. In this work, we present an experimental study where known prototype selection and abstraction algorithms are evaluated both on original data and a dimensionally reduced representation form of the same data from seven popular time series datasets. The experimental results demonstrate that prototype selection and abstraction algorithms, even when applied on dimensionally reduced data, can effectively reduce the computational cost of the classification process and the storage requirements for the training data, and, in some cases, improve classification accuracy.
KeywordsReduction Rate Concept Drift Neighbor Rule Training Item Prototype Selection
Unable to display preview. Download preview PDF.
- 4.Angiulli, F.: Fast condensed nearest neighbor rule. In: Proceedings of the 22nd International Conference on Machine Learning, ICML 2005, pp. 25–32. ACM, New York (2005)Google Scholar
- 10.Chou, C.H., Kuo, B.H., Chang, F.: The generalized condensed nearest neighbor rule as a data reduction method. In: Proceedings of the 18th International Conference on Pattern Recognition, ICPR 2006, vol. 02, pp. 556–559. IEEE Computer Society, Washington, DC (2006), http://dx.doi.org/10.1109/ICPR.2006.1119, doi:10.1109/ICPR.2006.1119Google Scholar
- 20.Lozano, M.: Data Reduction Techniques in Classification processes. (Phd Thesis). Universitat Jaume I (2007)Google Scholar
- 23.Ougiaroglou, S., Evangelidis, G.: RHC: Non-parametric cluster-based data reduction for efficient k-nn classification. Pattern Analysis and Applications (accepted, 2014)Google Scholar
- 26.Riquelme, J.C., Aguilar-Ruiz, J.S., Toro, M.: Finding representative patterns with ordered projections. Pattern Recognition 36(4), 1009–1018 (2003), http://www.sciencedirect.com/science/article/pii/S003132030200119X, doi:http://dx.doi.org/10.1016/S0031-32030200119-X
- 30.Toussaint, G.: Proximity graphs for nearest neighbor decision rules: Recent progress. In: 34th Symposium on the INTERFACE, pp. 17–20 (2002)Google Scholar
- 32.Tsymbal, A.: The problem of concept drift: definitions and related work. Tech. Rep. TCD-CS-2004-15, The University of Dublin, Trinity College, Department of Computer Science, Dublin, Ireland (2004)Google Scholar
- 34.Xi, X., Keogh, E., Shelton, C., Wei, L., Ratanamahatana, C.A.: Fast time series classification using numerosity reduction. In: Proceedings of the 23rd International Conference on Machine Learning, ICML 2006, pp. 1033–1040. ACM, New York (2006), http://doi.acm.org/10.1145/1143844.1143974, doi:10.1145/1143844.1143974