  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.
