Title: Multi-objective genetic motif discovery technique for time series classification

Authors: E. Ramanujam; S. Padmavathi

Addresses: Department of Information Technology, Thiagarajar College of Engineering, Madurai – 625015, Tamil Nadu, India ' Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai – 625015, Tamil Nadu, India

Abstract: Time series is a sequence of continuous and unbounded group of data observations recorded from many applications. Time series motif discovery is an essential and important task in time series mining. Discovering motifs in time series has attracted the researcher's attention for efficient time series classification problems and several algorithms have been proposed to solve the problem. However, these algorithms depend on predefined parameters like support, confidence, and length of the motif and they are sensitive to the parameters. To overcome the challenge, this paper proposes a multi-objective genetic algorithm to discover a good trade-off between representative and interesting motif. The discovered motifs are validated for their potential interest in time series classification using nearest neighbour classifier. Extensive experiments show that the proposed approach can efficiently discover motifs with different lengths and more accurate than state-of-the-art time series techniques. The paper also demonstrates the efficiency of motif discovery in classifying the large time series medical data from MIT-BIH-arrhythmia database.

Keywords: multi-objective genetic algorithms; MOGA; time series classification; UCR archive; arrhythmia; motif discovery; bioinformatics; nearest neighbour classifier; data mining.

DOI: 10.1504/IJBIDM.2016.082214

International Journal of Business Intelligence and Data Mining, 2016 Vol.11 No.4, pp.318 - 337

Received: 06 Jul 2016
Accepted: 18 Oct 2016
Published online: 12 Feb 2017 *

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