Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate ...

Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate ...

A Comparative Study of Various Supervised Feature. Selection Methods for Spam Classification. Shrawan Kumar Trivedi. Information System. BML Munjal University ...

Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate ...

In this paper, four machine learning algorithms which are NB, NN, SVM and RVM are proposed as dynamic anti-spam filtering methods to compare their performances.

Results of this study shows that RF is the excellent feature selection technique amongst other in terms of classification accuracy and false positive rate ...

The current study evaluates the effectiveness and efficiency of various machine learning techniques which include K-NN, Decision tree, random forest,. Naive ...

The paper presents a novel comparative study of redundant feature detection based feature selection methods. Experiments on several benchmark data sets ...

Feb 15, 2019 · In this study, a new backward elimination approach is proposed for feature selection. The main idea of this method is measuring the impact of ...

In the present study, various classification techniques for SMS spam detection have been explored such as Naive Bayes, Support Vector Machines (SVM), Decision ...