Boosting interval based literals¹

Article type: Research Article

Authors: Rodríguez, Juan J. | Alonso, Carlos J. | Boström, Henrik

Affiliations: Escuala Politecnica Superior, 09006 Burgos, Spain

Note: [1] This work has been supported by the Spanish CYCIT project TAP 99-0344.

Abstract: A supervised classification method for time series, even multivariate, is presented. It is based on boosting very simple classifiers: clauses with one literal in the body. The background predicates are based on temporal intervals. Two types of predicates are used: i) relative predicates, such as “increases” and “stays”, and ii) region predicates, such as “always” and “sometime”, which operate over regions in the domain of the variable. Experiments on different data sets, several of them obtained from the UCI ML and KDD repositories, show that the proposed method is highly competitive with previous approaches.

Keywords: time series classification, interval based literals, boosting, machine learning

DOI: 10.3233/IDA-2001-5305

Journal: Intelligent Data Analysis, vol. 5, no. 3, pp. 245-262, 2001