  Random convolution kernel transform (Rocket) is a fast, efficient, and novel
approach for time series feature extraction using a large number of independent
randomly initialized 1-D convolution kernels of different configurations. The
output of the convolution operation on each time series is represented by a
partial positive value (PPV). A concatenation of PPVs from all kernels is the
input feature vector to a Ridge regression classifier. Unlike typical deep
learning models, the kernels are not trained and there is no weighted/trainable
connection between kernels or concatenated features and the classifier. Since
these kernels are generated randomly, a portion of these kernels may not
positively contribute in performance of the model. Hence, selection of the most
important kernels and pruning the redundant and less important ones is
necessary to reduce computational complexity and accelerate inference of Rocket
for applications on the edge devices. Selection of these kernels is a
combinatorial optimization problem. In this paper, we propose a scheme for
selecting these kernels while maintaining the classification performance.
First, the original model is pre-trained at full capacity. Then, a population
of binary candidate state vectors is initialized where each element of a vector
represents the active/inactive status of a kernel. A population-based
optimization algorithm evolves the population in order to find a best state
vector which minimizes the number of active kernels while maximizing the
accuracy of the classifier. This activation function is a linear combination of
the total number of active kernels and the classification accuracy of the
pre-trained classifier with the active kernels. Finally, the selected kernels
in the best state vector are utilized to train the Ridge regression classifier
with the selected kernels.
