Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Swarm intelligence
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Machine Learning
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Exploring extended particle swarms: a genetic programming approach
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Extending particle swarm optimisers with self-organized criticality
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
A condensed polynomial neural network for classification using swarm intelligence
Applied Soft Computing
Filter-based optimization techniques for selection of feature subsets in ensemble systems
Expert Systems with Applications: An International Journal
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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.