  The typical process for classifying and submitting a newly sequenced virus to
the NCBI database involves two steps. First, a BLAST search is performed to
determine likely family candidates. That is followed by checking the candidate
families with the Pairwise Sequence Alignment tool for similar species. The
submitter's judgement is then used to determine the most likely species
classification. The aim of this paper is to show that this process can be
automated into a fast, accurate, one-step process using the proposed
alignment-free method and properly implemented machine learning techniques.
  We present a new family of alignment-free vectorizations of the genome, the
generalized vector, that maintains the speed of existing alignment-free methods
while outperforming all available methods. This new alignment-free
vectorization uses the frequency of genomic words (k-mers), as is done in the
composition vector, and incorporates descriptive statistics of those k-mers'
positional information, as inspired by the natural vector.
  We analyze 5 different characterizations of genome similarity using
$k$-nearest neighbor classification, and evaluate these on two collections of
viruses totaling over 10,000 viruses. We show that our proposed method performs
better than, or as well as, other methods at every level of the phylogenetic
hierarchy.
  The data and R code is available upon request.
