Anti-Spam Filtering Using Neural Networks and Baysian Classifiers

Using GMDH-based networks for improved spam detection and email feature analysis

“…Abductive networks, Multi-layer perceptron neural networks (see Table 6 the naïve Bayesian classifier (see Table 7 in [48]). Our comparisons with their best models are based on classification accuracy (Acc), false-positive rate (FPR), false-negative rate (FNR), spam recall (SR), spam precision (SP) and F-measure (FM).…”

“…The effectiveness of using multilayer perceptron neural networks and naïve Bayesian classifiers has been recently evaluated for spam filtering on the spambase dataset used in this paper [48]. In [48], the dataset was randomly shuffled and then partitioned into five independent subsets using 5-fold cross validation.…”

“…In each experiment, four subsets were used for training the classifier and the remaining subset was used for evaluation. The authors of [48] examined various MLP architectures to determine the best classifier model which was then tested for various cutoff threshold values (see Table 6 in [48]). They also tested 21,23,25,16,19,56,5,24,27,3,11,26,17,37, 57, 8} for model 3 in Table 8 as determined by the genetic learning algorithm.…”

“…In [48], the dataset was randomly shuffled and then partitioned into five independent subsets using 5-fold cross validation. Five experiments were conducted.…”

“…Therefore, they can be more effective in dealing with spammers' tactics. Recently, various machine-learning techniques have been used for spam filtering, including support vector machines [13,22,46], memory-based learning [6,35,41], Ripper rule-based learning [12,33], boosting decision trees [10], rough sets [49], neural networks [11,48], Bayesian classifiers [5,6,23,33,34,36,37], and fuzzy logic [17,32]. Among these methods, Bayesian classifiers have been widely applied as one of the most effective spam filters [19,23].…”