Stochastic Methods to Find Maximum Likelihood for Spam E-mail Classification

Abstract

The increasing volume of unsolicited bulk e-mails leads to the need for reliable stochastic spam detection methods for the classification of the received sequence of e-mails. When a sequence of emails is received by a recipient during a time period, the spam filters have already classified them as spam or not spam. Due to the dynamic nature of the spam, there might be emails marked as not spam but are actually real spams and vice versa. For the sake of security, it is important to be able to detect real spam emails. This paper utilizes stochastic methods to refine the preliminary spam detection and to find maximum likelihood for spam e-mail classification. The method is based on the Bayesian theorem, hidden Markov model (HMM), and the Viterbi algorithm.

Indirect Recognition of Predefined Human Activities

“…In general, these models are based on variables (also known as predictors) that are most likely to influence the outcome [ 26 ]. Predictive models are widely applied in various applications such as weather forecasting [ 27 , 28 , 29 ], Bayesian spam filters [ 30 , 31 , 32 , 33 ], business [ 34 , 35 , 36 , 37 ], and fraud detection [ 38 , 39 , 40 ]. Predictive models typically include a machine learning algorithm that learns certain properties from a training dataset.…”