One-Class Learning Time-Series Shapelets
Akihiro Yamaguchi, Takeichiro Nishikawa · 2018
Shapelets are time-series segments effective for classifying time-series datasets. In recent years, the discovery of shapelets by classifier learning has been studied. Methods for shapelet discovery have attracted great interest because they provide not only interpretable results but also superior classifi-cation performance. However, they do not consider imbalanced classifications between majority and minority classes, which may occur in actual applications (e.g., anomaly detection). Our aim is to learn shapelets and classifiers using only training data for the majority class without the minority class. We propose a method called one-class learning time-series shapelets (OCLTS). OCLTS efficiently and simultaneously optimizes both the shapelets and a non-linear classifier based on a one-class support vector machine by a stochastic sub-gradient descent algorithm. Experimental results show the method's effectiveness for interpretability and imbalanced binary classification.