Random Pairwise Shapelets Forest

TL;DR

This paper introduces Random Pairwise Shapelets Forest (RPSF), a more efficient and interpretable shapelet-based ensemble method for time series classification that improves accuracy and training speed.

Contribution

RPSF combines pairs of shapelets from different classes, omits threshold searching, and uses a new discriminability metric to enhance efficiency, accuracy, and interpretability.

Findings

RPSF outperforms existing shapelet-based forests in accuracy.

RPSF reduces training time significantly.

Case studies confirm improved interpretability.

Abstract

Shapelet is a discriminative subsequence of time series. An advanced shapelet-based method is to embed shapelet into accurate and fast random forest. However, it shows several limitations. First, random shapelet forest requires a large training cost for split threshold searching. Second, a single shapelet provides limited information for only one branch of the decision tree, resulting in insufficient accuracy and interpretability. Third, randomized ensemble causes interpretability declining. For that, this paper presents Random Pairwise Shapelets Forest (RPSF). RPSF combines a pair of shapelets from different classes to construct random forest. It omits threshold searching to be more efficient, includes more information for each node of the forest to be more effective. Moreover, a discriminability metric, Decomposed Mean Decrease Impurity (DMDI), is proposed to identify influential region for every class. Extensive experiments show RPSF improves the accuracy and training speed of shapelet-based forest. Case studies demonstrate the interpretability of our method.