An Efficient Feature Selection Algorithm Based on Hybrid Clonal Selection Genetic Strategy for Text Categorization

摘要

Feature selection is commonly used to reduce dimensionality of datasets with thousands of features which would be impossible to process further. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present a new feature selection algorithm for text categorization, called hybrid clonal selection genetic algorithm (HCSGA). Our experimental results, comparing HCSGA with an extensive and representative list of feature selection algorithms, show that HCSGA leads to a considerable increase in the classification accuracy, and is faster than the existing feature selection algorithms.