Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method

TL;DR

This paper introduces a novel diversified top-k shapelets extraction method integrated with ELM for improved time series classification, automatically determining the optimal number of shapelets to enhance accuracy and interpretability.

Contribution

It proposes a new diversified top-k shapelets extraction algorithm and integrates it with ELM, enabling automatic k selection and improved classification performance.

Findings

Significantly outperforms traditional ELM in accuracy.

Demonstrates improved efficiency in shapelet extraction.

Enhances interpretability of time series classification.

Abstract

ELM (Extreme Learning Machine) is a single hidden layer feed-forward network, where the weights between input and hidden layer are initialized randomly. ELM is efficient due to its utilization of the analytical approach to compute weights between hidden and output layer. However, ELM still fails to output the semantic classification outcome. To address such limitation, in this paper, we propose a diversified top-k shapelets transform framework, where the shapelets are the subsequences i.e., the best representative and interpretative features of each class. As we identified, the most challenge problems are how to extract the best k shapelets in original candidate sets and how to automatically determine the k value. Specifically, we first define the similar shapelets and diversified top-k shapelets to construct diversity shapelets graph. Then, a novel diversity graph based top-k shapelets extraction algorithm named as \textbf{DivTopkshapelets}\ is proposed to search top-k diversified shapelets. Finally, we propose a shapelets transformed ELM algorithm named as \textbf{DivShapELM} to automatically determine the k value, which is further utilized for time series classification. The experimental results over public data sets demonstrate that the proposed approach significantly outperforms traditional ELM algorithm in terms of effectiveness and efficiency.