Using Adaboost and Stochastic gradient descent (SGD) Algorithms with R and Orange Software for Filtering E-mail Spam
Huwaida Tagelsir Ibrahim Elshoush, Esraa A. Dinar · 2019
With the increasing usage of electronic emails, the ratio of spam is increasing day by day. Thus, spam emails have become a major threat that lowers the usage of electronic emails as a way for communication. There are several machine learning techniques that provide email spam filtering methods, such as Naive Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN) and Decision tree (DT). This paper considers different machine learning techniques to filter spam emails, specifically Adaboost and Stochastic Gradient Descent (SGD). R tool was used for the pre-processing stage. Adaboost and SGD were implemented in Orange software for building the classifiers. Using Orange tool, the experimental results showed that the algorithms Adaboost and stochastic gradient descent (SGD) provided true positive value of 100 % and 98.1% respectively and false positive rates of 0.0% and 1.9% respectively. The good accuracy of these algorithms and the favorable results put them among the best choices of spam filtering methods.