An Optimized Feature Selection Technique For Email Classification
Olaleye Oludare, Olabiyisi Stephen, Olaniyan Ayodele, Fagbola Temitayo · International journal of scientific and technology research · 2014
In machine learning, feature selection is a problem of global combinatorial optimization resulting in poor predictions and high computational overhead due to irrelevant and redundant features in the dataset. The Support Vector Machine (SVM) is a classifier suitable to deal with feature problems but cannot efficiently handle large e-mail dataset. In this research work, the feature selection in SVM was optimized using Particle Swarm Optimization (PSO). The results obtained from this study showed that the optimized SVM technique gave a classification accuracy of 80.44% in 2.06 seconds while SVM gave an accuracy of 68.34% in 6.33 seconds for email dataset of 1000. Using the 3000 e-mail dataset, the classification accuracy and computational time of the optimized SVM technique and SVM were 90.56%, 0.56 second and 46.71%, 60.16 seconds respectively. Similarly, 93.19%, 0.19 second and 18.02%, 91.47 seconds were obtained for optimized SVM technique and SVM respectively using 6000 e-mail dataset. In conclusion, the results obtained demonstrate that the optimized SVM technique had better classification accuracy with less computational time than SVM. The optimized SVM technique exhibited better performance with large e-mail dataset thereby eliminating the drawbacks of SVM.