Time series gene expression data classification via L1-norm temporal SVM

Abstract

Machine learning methods have been successfully applied to the phenotype classification of many diseases based on static gene expression measurements. More recently microarray data have been collected over time, making available datasets composed by time series of expression gene profiles. In this paper we propose a new method for time series classification, based on a temporal extension of L 1-norm support vector machines, that uses dynamic time warping distance for measuring time series similarity. This results in a mixed-integer optimization model which is solved by a sequential approximation algorithm. Computational tests performed on two benchmark datasets indicate the effectiveness of the proposed method compared to other techniques, and the general usefulness of the approaches based on dynamic time warping for labeling time series gene expression data. © 2010 Springer-Verlag.