  Machine learning (ML) models, such as SVM, for tasks like classification and
clustering of sequences, require a definition of distance/similarity between
pairs of sequences. Several methods have been proposed to compute the
similarity between sequences, such as the exact approach that counts the number
of matches between $k$-mers (sub-sequences of length $k$) and an approximate
approach that estimates pairwise similarity scores. Although exact methods
yield better classification performance, they pose high computational costs,
limiting their applicability to a small number of sequences. The approximate
algorithms are proven to be more scalable and perform comparably to (sometimes
better than) the exact methods -- they are designed in a "general" way to deal
with different types of sequences (e.g., music, protein, etc.). Although
general applicability is a desired property of an algorithm, it is not the case
in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic,
there is a need for an approach that can deal specifically with the
coronavirus. To this end, we propose a series of ways to improve the
performance of the approximate kernel (using minimizers and information gain)
in order to enhance its predictive performance pm coronavirus sequences. More
specifically, we improve the quality of the approximate kernel using domain
knowledge (computed using information gain) and efficient preprocessing (using
minimizers computation) to classify coronavirus spike protein sequences
corresponding to different variants (e.g., Alpha, Beta, Gamma). We report
results using different classification and clustering algorithms and evaluate
their performance using multiple evaluation metrics. Using two datasets, we
show that our proposed method helps improve the kernel's performance compared
to the baseline and state-of-the-art approaches in the healthcare domain.
