Using Adaboost and Stochastic gradient descent (SGD) Algorithms with R and Orange Software for Filtering Email Spam 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), KNearest 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 preprocessing 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.

Inquiry Spam Detection via Jointly Exploiting TemporalCategorical Behavior and Linguistics Inquiry performs one of the backbones in current Ecommerce websites. Detecting spams in inquiries is essential for these platforms but is surprisingly underexplored by current research. In this paper, we propose a system coined ISTBEL to detect spam inquiries. Motivated by the observations on both behavioral and linguistic differences between spammers and benign users, ISTBEL jointly utilizes the temporalcategorical behavioral sequence and text sequence to accomplish its purpose. At its heart, a variant of LSTM equipped interactive attentions is devised to strengthen the coherence between the categorical and temporal behaviors. A characterlevel CNN is adopted to capture the capricious linguistic patterns. These two modules are integrated and collaboratively contribute to the final predictions. Our system is trained through an endtoend manner and can directly utilize raw data as input without tedious feature engineering. We have applied the system to inquiry spam detection on Alibaba.com, and results show ISTBEL is promising in detection spammers and outperforms baselines by a large margin. Meanwhile, ISTBEL can be easily applied to other applications which contain both behavioral and linguistic information.