Time Series Classification Based on Attributes Weighted Sample Reducing KNN

KNN is widely used in classification, but it could not gain good performance for multiattribute time series classifying. According to the characteristic of multiattribute time series and shortage of KNN, the attributes weighted sample reducing KNN classification approach—WRKNN is proposed. Two major aspects are improved for KNN classification, one is to give weight to the attributes of time series; the other one is to reduce the training set to relative equal density based on weighted distance. A equally distributed training data set is obtained by the improved KNN approach, and the number of training samples is decreased at the same time, hence the efficiency and accuracy is enhanced. At last, the feasible of WRKNN is tested by the experiment.

[1]  Shiwen Yu,et al.  An Improved k-Nearest Neighbor Algorithm for Text Categorization , 2003, ArXiv.

[2]  Roque Marín,et al.  On knowledge-based fuzzy classifiers: A medical case study , 1991 .

[3]  C. G. Hilborn,et al.  The Condensed Nearest Neighbor Rule , 1967 .

[4]  Ludmila I. Kuncheva,et al.  Fitness functions in editing k-NN reference set by genetic algorithms , 1997, Pattern Recognit..

[5]  Gang Chen,et al.  A Fast Document Classification Algorithm Based on Improved KNN , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[6]  Mohammed Waleed Kadous,et al.  Temporal classification: extending the classification paradigm to multivariate time series , 2002 .

[7]  Sang-Chan Park,et al.  A hybrid approach of neural network and memory-based learning to data mining , 2000, IEEE Trans. Neural Networks Learn. Syst..

[8]  Huan Liu,et al.  Neural-network feature selector , 1997, IEEE Trans. Neural Networks.

[9]  Peter E. Hart,et al.  The condensed nearest neighbor rule (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[10]  Jun-li Lu,et al.  Research and application on KNN method based on cluster before classification , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[11]  Chin-Liang Chang,et al.  Finding Prototypes For Nearest Neighbor Classifiers , 1974, IEEE Transactions on Computers.

[12]  Philip R. Thrift,et al.  Hybrid neural network classifiers for automatic target detection , 1993 .

[13]  James C. Bezdek,et al.  Multiple-prototype classifier design , 1998, IEEE Trans. Syst. Man Cybern. Part C.

[14]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

[15]  Michael T. Manry,et al.  Iterative improvement of a nearest neighbor classifier , 1991, Neural Networks.

[16]  Ludmila I. Kuncheva,et al.  Editing for the k-nearest neighbors rule by a genetic algorithm , 1995, Pattern Recognit. Lett..