Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Further Research on Feature Selection and Classification Using Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Feature Subset Selection By Estimation Of Distribution Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An introduction to variable and feature selection
The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Improving a Pittsburgh Leant Fuzzy Rule Base using Feature Subset Selection
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
An Immune-Inspired Approach to Bayesian Networks
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Using learning to facilitate the evolution of features for recognizing visual concepts
Evolutionary Computation
A review of feature selection techniques in bioinformatics
Bioinformatics
Feature Subset Selection by Means of a Bayesian Artificial Immune System
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
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Bayesian networks have been widely applied to the feature selection problem. The existing approaches learn a Bayesian network from the available dataset and, afterward, utilize the Markov Blanket of the target feature as the criterion to select the relevant features. The Bayesian network learning can be viewed as a search and optimization procedure, where a search mechanism explores the space of all network structures while a scoring metric evaluates each candidate solution based on the likelihood. This paper investigates the application of an immune-inspired algorithm as the search procedure for obtaining high-quality Bayesian networks, motivated by the dynamical control of the population size and diversity along the search. Due to the resulting multimodal search capability, in a single run of the algorithm several subsets of features are obtained. Experiments on ten datasets were carried out in order to evaluate the proposed methodology in classification problems, and reduced-size subsets of features were produced.