  Most neural network-based classifiers extract features using several hidden
layers and make predictions at the output layer by utilizing these extracted
features. We observe that not all features are equally pronounced in all
classes; we call such features class-specific features. Existing models do not
fully utilize the class-specific differences in features as they feed all
extracted features from the hidden layers equally to the output layers. Recent
attention mechanisms allow giving different emphasis (or attention) to
different features, but these attention models are themselves class-agnostic.
In this paper, we propose a novel class-specific attention (CSA) module to
capture significant class-specific features and improve the overall
classification performance of time series. The CSA module is designed in a way
such that it can be adopted in existing neural network (NN) based models to
conduct time series classification. In the experiments, this module is plugged
into five start-of-the-art neural network models for time series classification
to test its effectiveness by using 40 different real datasets. Extensive
experiments show that an NN model embedded with the CSA module can improve the
base model in most cases and the accuracy improvement can be up to 42%. Our
statistical analysis show that the performance of an NN model embedding the CSA
module is better than the base NN model on 67% of MTS and 80% of UTS test cases
and is significantly better on 11% of MTS and 13% of UTS test cases.
