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GECCO
2006
Springer

A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set

11 years 5 months ago
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Many data mining applications involve the task of building 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 accuracy and reliability of a classification or prediction model. Superfluous variables can also increase the costs of building a model - particularly on large data sets. We propose a discrete Particle Swarm Optimization (PSO) algorithm designed for attribute selection. The proposed algorithm deals with discrete variables, and its population of candidate solutions 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 attributes in a bioinformatics data set. The criteria used for comparison are: (1) maximizing predictive accuracy; and (2) finding the smallest subset of attributes. Cate...
Elon S. Correa, Alex Alves Freitas, Colin G. Johns
Added 23 Aug 2010
Updated 23 Aug 2010
Type Conference
Year 2006
Where GECCO
Authors Elon S. Correa, Alex Alves Freitas, Colin G. Johnson
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