A New Feature Selection Algorithm for Efficient Spam Filtering using Adaboost and Hashing Techniques

Email spam is one of the significant issues of the today's Internet. A steady measure of the spammer attack, which brings harm to corporate and bothering individual clients. There are lots of strategies to battle against the spam mail. Being as an effective procedure, it is appreciative to experience and channel the email spam. In this paper, we proposed a new approach by utilizing, the hashing algorithm with AdaBoost technique, an AdaBoost Technique classifies the text and image values. The proposed approach essentially accelerates the procedure of Adaptive Boosting (Adaboost) by lessening the amount of information focuses. This guarantees that Adaboost can prepare productive and insignificant misfortune of precision. The result of the proposed system is a decreased set of delegate preparing focuses, hence diminishing the general computational complexity of preparing and expanding the speed of the training process. This is will be used for large scale application.