A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Term Weighting Approaches in Automatic Text Retrieval
Term Weighting Approaches in Automatic Text Retrieval
Feature selection from huge feature sets in the context of computer vision
Feature selection from huge feature sets in the context of computer vision
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Ant colony optimization theory: a survey
Theoretical Computer Science
A novel feature selection algorithm for text categorization
Expert Systems with Applications: An International Journal
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Beyond the bag of words: a text representation for sentence selection
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Feature selection for dimensionality reduction
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
Semi-supervised text categorization: Exploiting unlabeled data using ensemble learning algorithms
Intelligent Data Analysis
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In our previous work we have proposed an ant colony optimization (ACO) algorithm for feature selection. In this paper, we hybridize the algorithm with a genetic algorithm (GA) to obtain excellent features of two algorithms by synthesizing them. Proposed algorithm is applied to a challenging feature selection problem. This is a data mining problem involving the categorization of text documents. We report the extensive comparison between our proposed algorithm and three existing algorithms - ACO-based, information gain (IG) and CHI algorithms proposed in the literature. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. Experimentations are carried out on Reuters-21578 dataset. Simulation results on Reuters-21578 dataset show the superiority of the proposed algorithm.