Boosting Interval-Based Time Series Classifiers
نویسندگان
چکیده
A supervised classification method for time series, even multivariable, is proposed. It is based on boosting very simple classifiers. They are formed only by one literal. The used predicates, such as “always” and “sometime” operate over temporal intervals and regions in the dominion of the values of the variable. These regions are obtained previously, using discretization techniques. The experimental validation of the method has been done using different data sets, some of them obtained from the UCI repositories. The results are very competitive with the obtained 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. Keywords—Time Series, Boosting, Machine Learning
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