  Viral sequence classification is an important task in pathogen detection,
epidemiological surveys and evolutionary studies. Statistical learning methods
are widely used to classify and identify viral sequences in samples from
environments. These methods face several challenges associated with the nature
and properties of viral genomes such as recombination, mutation rate and
diversity. Also, new generations of sequencing technologies rise other
difficulties by generating massive amounts of fragmented sequences. While
linear classifiers are often used to classify viruses, there is a lack of
exploration of the accuracy space of existing models in the context of
alignment free approaches. In this study, we present an exhaustive assessment
procedure exploring the power of linear classifiers in genotyping and subtyping
partial and complete genomes. It is applied to the Hepatitis C viruses (HCV).
Several variables are considered in this investigation such as classifier types
(generative and discriminative) and their hyper-parameters (smoothing value and
regularization penalty function), the classification task (genotyping and
subtyping), the length of the tested sequences (partial and complete) and the
length of k-mer words. Overall, several classifiers perform well given a set of
precise combination of the experimental variables mentioned above. Finally, we
provide the procedure and benchmark data to allow for more robust assessment of
classification from virus genomes.
