  A panel of large number of common Single Nucleotide Polymorphisms (SNPs)
distributed across an entire porcine genome has been widely used to represent
genetic variability of pig. With the advent of SNP-array technology, a
genome-wide genetic profile of a specimen can be easily observed. Among the
large number of such variations, there exist a much smaller subset of the SNP
panel that could equally be used to correctly identify the corresponding breed.
This work presents a SNP selection heuristic that can still be used effectively
in the breed classification process. The proposed feature selection was done by
the approach of combining a filter method and a wrapper method--information
gain method and genetic algorithm--plus a feature frequency selection step,
while classification was done by support vector machine. The approach was able
to reduce the number of significant SNPs to 0.86 % of the total number of SNPs
in a swine dataset and provided a high classification accuracy of 94.80 %.
