International Journal of Computer Science Issues

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Feature selection is a problem of global combinatorial optimization in machine learning in which subsets of relevant features are selected to realize robust learning models. The implying of irrelevant and redundant features in the dataset can result in poor predictions and misclassification process. Thus, selecting relevant feature subsets can help reduce the computational cost of feature measurement, speed up learning process and improve model interpretability. Rough sets Method in classification has proven inefficient in its inability to produce accurate classification results in the face of large e-mail dataset while it also consumes a lot of computational resources. In this study, we present GRF- Genetics Rough Filter -a hybrid of Genetic Algorithm-Rough set feature selection technique is developed to optimize the Rough set classification parameters, the prediction accuracy and computation time. Spam assassin dataset was used to validate the performance of the proposed system. GRF showed remarkable improvements over Neural Network, Rough set and SVM methods in terms of classification accuracy.