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Combination of similarity measures for time series classification using genetic algorithms

Dohare, Deepti and Devi, Susheela S (2011) Combination of similarity measures for time series classification using genetic algorithms. In: 2011 IEEE Congress on Evolutionary Computation (CEC), 5-8 June 2011, New Orleans, LA.

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Official URL: http://dx.doi.org/10.1109/CEC.2011.5949646


Time series classification deals with the problem of classification of data that is multivariate in nature. This means that one or more of the attributes is in the form of a sequence. The notion of similarity or distance, used in time series data, is significant and affects the accuracy, time, and space complexity of the classification algorithm. There exist numerous similarity measures for time series data, but each of them has its own disadvantages. Instead of relying upon a single similarity measure, our aim is to find the near optimal solution to the classification problem by combining different similarity measures. In this work, we use genetic algorithms to combine the similarity measures so as to get the best performance. The weightage given to different similarity measures evolves over a number of generations so as to get the best combination. We test our approach on a number of benchmark time series datasets and present promising results.

Item Type: Conference Paper
Publisher: IEEE
Additional Information: Copyright of this article belongs to IEEE.USA
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 19 Mar 2013 10:25
Last Modified: 19 Mar 2013 10:25
URI: http://eprints.iisc.ac.in/id/eprint/46048

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