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A New Discrete Particle Swarm Algorithm Applied to Attribute Selection in a Bioinformatics Data Set

Correa, Elon S. and Freitas, Alex A. and Johnson, Colin G. (2008) A New Discrete Particle Swarm Algorithm Applied to Attribute Selection in a Bioinformatics Data Set. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation. ACM, New York, USA, pp. 35-42. ISBN 1-59593-186-4. (doi:10.1145/1143997.1144003) (KAR id:71009)

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Many data mining applications involve the task of build- ing a model for predictive classification. The goal of such a model is to classify examples (records or data instances) into classes or categories of the same type. The use of variables (attributes) not related to the classes can reduce the accu- racy and reliability of a classification or prediction model. Superfluous variables can also increase the costs of build- ing a model - particularly on large data sets. We propose a discrete Particle Swarm Optimization (PSO) algorithm de- signed for attribute selection. The proposed algorithm deals with discrete variables, and its population of candidate solu- tions contains particles of different sizes. The performance of this algorithm is compared with the performance of a standard binary PSO algorithm on the task of selecting at- tributes in a bioinformatics data set. The criteria used for comparison are: (1) maximizing predictive accuracy; and (2) finding the smallest subset of attributes.

Item Type: Book section
DOI/Identification number: 10.1145/1143997.1144003
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Q Science > QH Natural history > QH324.2 Computational biology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Colin Johnson
Date Deposited: 13 Dec 2018 15:58 UTC
Last Modified: 16 Nov 2021 10:25 UTC
Resource URI: (The current URI for this page, for reference purposes)
Freitas, Alex A.:
Johnson, Colin G.:
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