Adapting ELM to Time Series Classification: A Novel Diversified Top-k Shapelets Extraction Method
This work addresses interpretability and performance issues in time series classification for researchers and practitioners, though it is incremental as it builds on existing ELM and shapelets methods.
The paper tackled the problem of ELM's lack of semantic classification outcomes in time series classification by proposing a diversified top-k shapelets extraction method, resulting in significant improvements in effectiveness and efficiency over traditional ELM on public datasets.
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.