Using online linear classifiers to filter spam Emails

The performance of two online linear classifiers - the Perceptron and Littlestone’s Winnow – is explored for two anti-spam filtering benchmark corpora - PU1 and Ling-Spam. We study the performance for varying numbers of features, along with three different feature selection methods: Information Gain (IG), Document Frequency (DF) and Odds Ratio. The size of the training set and the number of training iterations are also investigated for both classifiers. The experimental results show that both the Perceptron and Winnow perform much better when using IG or DF than using Odds Ratio. It is further demonstrated that when using IG or DF, the classifiers are insensitive to the number of features and the number of training iterations, and not greatly sensitive to the size of training set. Winnow is shown to slightly outperform the Perceptron. It is also demonstrated that both of these online classifiers perform much better than a standard Naïve Bayes method. The theoretical and implementation computational complexity of these two classifiers are very low, and they are very easily adaptively updated. They outperform most of the published results, while being significantly easier to train and adapt. The analysis and promising experimental results indicate that the Perceptron and Winnow are two very competitive classifiers for anti-spam filtering

The Information of Spam

This paper explores the value of information contained in spam tweets as it pertains to prediction accuracy. As a case study, tweets discussing Bitcoin were collected and used to predict the rise and fall of Bitcoin value. Precision of prediction both with and without spam tweets, as identified by a naive Bayesian spam filter, were measured. Results showed a minor increase in accuracy when spam tweets were included, indicating that spam messages likely contain information valuable for prediction of market fluctuations

Learning to Filter Text in Forum Malay Message using Naive Bayesian Technique

Applying the basic filtering technique in forum application has been discussed in [I]. The paper explains about me use of the basic naive Bayesian algorithm to classify forum messages whether clean or bad where clean message has no bad words, while bad message contains at least one bad word. In this Final Year Project paper, the application ofthe algorithm in the filtering forum messages will be discussed in the attempt to apply learning to filter forum messages

Bayesian Spam Detection

Spammers always find new ways to get spammy content to the public. Very commonly this is accomplished by using email, social media, or advertisements. According to a 2011 report by the Messaging Anti-Abuse Working Group roughly 90% of all emails in the United States are spam. This is why we will be taking a more detailed look at email spam. Spam filters have been getting better at detecting spam and removing it, but no method is able to block 100% of it. Because of this, many different methods of text classification have been developed, including a group of classifiers that use a Bayesian approach. The Bayesian approach to spam filtering was one of the earliest methods used to filter spam, and it remains relevant to this day. In this paper we will analyze 2 specific optimizations of Naive Bayes text classification and spam filtering, looking at the differences between them and how they have been used in practice. This paper will show that Bayesian filtering can be simply implemented for a reasonably accurate text classifier and that it can be modified to make a significant impact on the accuracy of the filter. A variety of applications will be explored as well

A Survey of Existing E-mail Spam Filtering Methods Considering Machine Learning Techniques

E-mail is one of the most secure medium for online communication and transferring data or messages through the web. An overgrowing increase in popularity, the number of unsolicited data has also increased rapidly. To filtering data, different approaches exist which automatically detect and remove these untenable messages. There are several numbers of email spam filtering technique such as Knowledge-based technique, Clustering techniques, Learningbased technique, Heuristic processes and so on. This paper illustrates a survey of different existing email spam filtering system regarding Machine Learning Technique (MLT) such as Naive Bayes, SVM, K-Nearest Neighbor, Bayes Additive Regression, KNN Tree, and rules. However, here we present the classification, evaluation and comparison of different email spam filtering system and summarize the overall scenario regarding accuracy rate of different existing approache