Artificial intelligence and molecular biology
Artificial intelligence and molecular biology
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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A successful interpretation of data goes through discovering crucial relationships between variables. Such a task can be accomplished by a Bayesian network. The dark side is that, when lots of variables are involved, the learning of the network slows down and may lead to wrong results. In this study, we demonstrate the feasibility of applying an existing Particle Swarm Optimization (PSO)-based approach to feature selection for filtering the irrelevant attributes of the dataset, resulting in a fine Bayesian network built with the K2 algorithm. Empirical tests carried out with real data coming from the bioinformatics domain bear out that the PSO fitness function is in a straight concordance to the most widely known validation measures for classification.