Particle swarm and Bayesian networks applied to attribute selection for protein functional classification

Correa, ES; Freitas, AA; Johnson, CG

Abstract

The Discrete Particle Swarm (DPSO) algorithm is an optimizationmethod that belongs to the fertile paradigm of Swarm Intelligence. The DPSO was designed for the task of attribute selection and it deals with discrete variables in a straightforward manner. This work extends the DPSO algorithm in two ways. First, we enable the DPSO to select attributes for a Bayesian network algorithm, which is a much more sophisticated algorithm than the Naive Bayes classifier previously used by this algorithm. Second, we apply the DPSO to a challenging protein functional classification data set, involving a large number of classes to be predicted. The performance of the DPSO is compared to the performance of a Binary PSO on the task of selecting attributes in this challenging data set. The criteria used for comparison are: (1) maximizing predictive accuracy; and (2) finding the smallest subset of attributes.

Citation

Correa, E., Freitas, A., & Johnson, C. (2007). Particle swarm and Bayesian networks applied to attribute selection for protein functional classification. In Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation - GECCO '07 (2651). ACM Digital Library. https://doi.org/10.1145/1274000.1274081

• Start Date: Jul 7, 2007
• End Date: Jul 11, 2007
• Publication Date: Jan 1, 2007
• Deposit Date: Feb 10, 2017
• Pages: 2651
• Book Title: Proceedings of the 2007 GECCO conference companion on Genetic and evolutionary computation - GECCO '07
• ISBN: 9781595936981
• DOI: https://doi.org/10.1145/1274000.1274081
• Publisher URL: http://dx.doi.org/10.1145/1274000.1274081
• Additional Information: Event Type : Conference