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.
Highlights
Most of the particle swarm algorithms present in the literature deal only with continuous variables [1,2,3]
The discrete particle swarm optimization (DPSO) algorithm did slightly better than the binary particle swarm optimization (PSO) algorithm in all class levels
The predictive accuracy attained by both versions of the PSO algorithm surpassed the predictive accuracy obtained by the baseline algorithm in all class levels
Summary
Most of the particle swarm algorithms present in the literature deal only with continuous variables [1,2,3]. This is a significant limitation because many optimization problems are set in a search space featuring discrete variables. The work in [5] proposed a discrete particle swarm optimization (PSO) algorithm for attribute selection in Data Mining. Hereafter, this algorithm will be refereed to as the discrete particle swarm optimization (DPSO) algorithm. The motivation behind the DPSO algorithm is to introduce a probability-like approach to particle swarm
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