Learning First Order Logic Time Series Classifiers: Rules and Boosting

摘要

A method for learning multivariate time series classifiers by inductive logic programming is presented. Two types of background predicate that are suited for this task are introduced: interval based predicates, such as always, and distance based, such as the euclidean distance. Special purpose techniques are presented that allow these predicates to be handled efficiently when performing top-down induction. Further-more, by employing boosting, the accuracy of the resulting classifiers can be improved significantly. Experiments on several different datasets show that the proposed method is highly competitive with previous approaches.