Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Global Optimization
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Text feature selection using ant colony optimization
Expert Systems with Applications: An International Journal
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High dimensionality of data is a limiting factor to data processing in many fields. It causes ambiguousness in identifying significant factors for data analysis. Dimension reduction is needed to separate irrelevant data from the desired data. This research proposes a novel method for dimension reduction based on artificial bee colony (ABC). The method employs swarm intelligence based on bee foraging model in order to select features that allow us to generate subsets of dimensions from the original high-dimensional data while the resulting subsets satisfy the defined objective. Support vector machine (SVM) is used in this study as fitness evaluation of ABC in classification problems. To evaluate our method, we tested it with five datasets and compared it with other dimension reduction algorithms. The result of this study shows that using ABC and SVM is suitable for reducing the dimension of data. Moreover, this approach provides efficient classification with high accuracy.