Machine Learning Methods for Spam E-Mail Classification

Awad, Wael A.; Elseuofi, S. M.

doi:10.5121/ijcsit.2011.3112

Cited by 79 publications

(49 citation statements)

References 13 publications

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“…Naï ve Bayes turned out to be the best classifier. The variation of accuracy, precision and recall with different number of features was not studied by the authors [12]. Another work by Kumar et [15], an open source data mining tool.…”

Section: Related Workmentioning

confidence: 99%

Impact of Feature Selection Technique on Email Classification

Sharaff¹,

Nagwani²,

Swami³

2015

IJKE

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Abstract-Being one of the most powerful and fastest way of communication, the popularity of email has led to untoward rise of email spam. Spam are unwanted and unsolicited messages and the subsequent rise of spam received by email users has become a serious security threat. Automatic filtering of spam emails, hence, is a promising and research worthy area whereupon extensive work has been reported about attempts to design machine learning based classifiers. Herein feature selection technique can be conveniently applied for developing efficient machine learning based classifiers. However, feature selection techniques provide a mechanism to identify suitable and relevant features (attributes) for any knowledge discovery task. The choice of selecting a suitable feature selection technique is always a key question of research. The present paper compares and discusses the effectiveness of two feature selection methods i.e. Chi-square and Info-gain on machine learning techniques namely Bayes algorithm, tree-based algorithm and support vector machine with a purpose to design a classifier for spam email filtering. The experiment is performed using 10-fold cross-validation and performance measures such as accuracy, precision, recall are used to compare the results.

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Section: Related Workmentioning

confidence: 99%

Impact of Feature Selection Technique on Email Classification

Sharaff¹,

Nagwani²,

Swami³

2015

IJKE

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“…Spam filters are employed to assist the user in deciding if an email is worth reading or not. There have been tremendous research efforts in this field that resulted in a lot of commercial spam filtering products, such as: methods for construction of filters to eliminate unwanted messages [1], comparison between the performances of machine learning-based classifiers in filtering email spam [2], [3].…”

Section: Introductionmentioning

confidence: 99%

“…The characteristics (e.g. topics, frequent terms) of spam e-mail vary rapidly over time as spammers always seek to invent new strategies to bypass spam filters [3]. One cannot develop a filter and immediately implement it, because it will not have any basis for classifying a document as spam or not spam.…”

Section: Introductionmentioning

confidence: 99%

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

Mansourbeigi

2019

Advances in Intelligent Systems and Computing

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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.

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“…Email classification has been considered as a serious problem for users and companies [3]. The tasks of email classifier have been divided into several subtasks involving data collection and presenting email message as well as email feature selection and feature dimensionality reduction [3]. The purpose of email classification is to distinguish spam and legitimate messages.…”

Section: Introductionmentioning

confidence: 99%

Feature Selection by Using Discrete Imperialist Competitive Algorithm to Spam Detection

kalaibar

Razavi

2014

IJCT

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Spam is a basic problem in electronic communications such as email systems in large scales and large number of weblogs and social networks. Due to the problems created by spams, much research has been carried out in this regard by using classification techniques. Redundant and high dimensional information are considered as a serious problem for these classification algorithms due to their high computation costs and using a memory. Reducing feature space results in representing an understandable model and using various methods. In this paper, the method of feature selection by using imperialist competitive algorithm has been presented. Decision tree and SVM classifications have been taken into account in classification phase. In order to prove the efficiency of this method, the results of evaluating data set of Spam Base have been compared with the algorithms proposed in this regard such as genetic algorithm. The results show that this method improves the efficiency of spam detection.

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Machine Learning Methods for Spam E-Mail Classification

Abstract: ABSTRACT

Cited by 79 publications

References 13 publications

Impact of Feature Selection Technique on Email Classification

Impact of Feature Selection Technique on Email Classification

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

Feature Selection by Using Discrete Imperialist Competitive Algorithm to Spam Detection

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