Abstract
The discrete particle swarm optimization (DPSO) algorithm is an
optimization technique which belongs to the fertile paradigm of Swarm Intelligence.
Designed for the task of attribute selection, the DPSO deals with discrete
variables in a straightforward manner. This work empowers the DPSO algorithm
by extending it in two ways. First, it enables the DPSO to select attributes for a
Bayesian network algorithm, which is more sophisticated than the Naive Bayes
classifier previously used by the original DPSO algorithm. Second, it applies the
DPSO to a set of challenging protein functional classification data, involving a
large number of classes to be predicted. The work then compares the performance
of the DPSO algorithm against the performance of a standard Binary PSO
algorithm on the task of selecting attributes on those data sets. The criteria used
for this comparison are (1) maximizing predictive accuracy and (2) finding the
smallest subset of attributes.