Particle swarm for attribute selection in Bayesian classification : an application to protein function prediction

Correa, ES; Freitas, AA; Johnson, CG

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.

Citation

Correa, E., Freitas, A., & Johnson, C. (2008). Particle swarm for attribute selection in Bayesian classification : an application to protein function prediction. Journal of Artificial Evolution and Applications, 2008, 1-12. https://doi.org/10.1155/2008/876746

• Journal Article Type: Article
• Acceptance Date: Jan 10, 2008
• Publication Date: Jan 1, 2008
• Deposit Date: Feb 10, 2017
• Publicly Available Date: Feb 10, 2017
• Journal: Journal of Artificial Evolution and Applications
• Print ISSN: 1687-6229
• Electronic ISSN: 1687-6237
• Publisher: Hindawi
• Volume: 2008
• Pages: 1-12
• DOI: https://doi.org/10.1155/2008/876746
• Publisher URL: http://dx.doi.org/10.1155/2008/876746
• Related Public URLs: https://www.hindawi.com/journals/jaea/