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
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
A novel ACO-GA hybrid algorithm for feature selection in protein function prediction
Expert Systems with Applications: An International Journal
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Discrete particle swarm optimisation for ontology alignment
Information Sciences: an International Journal
SVM classifier based feature selection using GA, ACO and PSO for siRNA design
ICSI'10 Proceedings of the First international conference on Advances in Swarm Intelligence - Volume Part II
Efficient ant colony optimization for image feature selection
Signal Processing
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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.