Abstract:

Classification is one of the basic tasks in data mining, and feature representation and similarity measurement act as the important basis of time series data mining. The symbolic aggregate approximation (SAX) is a typical symbolic representation method which is straightforward and very simple, and which can efficiently converts time series data to a symbolic representation with dimensionality/ noise reduction. But the potential of information loss can affect the accuracy of the classification results. Focusing on the SAX discretization method coupled with the bag of patterns (BOP) representation in classification task, we propose a multiphased approach framework using the AdaBoost algorithm and voting in ensemble learning   to remedy the information loss of the SAX representation. Experimental results show that the proposed method can improve classification accuracy greatly.

Key words: time series;SAX;classification;ensemble learning;multiphased