Applying Boosting to Similarity Literals for Time Series Classification

Authors:
Juan J. Rodríguez Diez;Carlos Alonso González
Affiliations:
-;-
Venue:
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Year:
2000

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Abstract

A supervised classification method for temporal series, even multivariate, is presented. It is based on boosting very simple classifiers, which consists only of one literal. The proposed predicates are based in similarity functions (i.e., euclidean and dynamic time warping) between time series. The experimental validation of the method has been done using different datasets, some of them obtained from the UCI repositories. The results are very competitive with the reported in previous works. Moreover, their comprehensibility is better than in other approaches with similar results, since the classifiers are formed by a weighted sequence of literals.