Handling Dimensionality Reduction in Spam E-Mail Classification (original) (raw)
Related papers
MACHINE LEARNING METHODS FOR SPAM E-MAIL CLASSIFICATION
The increasing volume of unsolicited bulk e-mail (also known as spam) has generated a need for reliable anti-spam filters. Machine learning techniques now days used to automatically filter the spam e-mail in a very successful rate. In this paper we review some of the most popular machine learning methods (Bayesian classification, k-NN, ANNs, SVMs, Artificial immune system and Rough sets) and of their applicability to the problem of spam Email classification. Descriptions of the algorithms are presented, and the comparison of their performance on the SpamAssassin spam corpus is presented.
Evaluation of the Performance for Popular Three Classifiers on Spam Email without using FS methods
WSEAS TRANSACTIONS ON SYSTEMS AND CONTROL
Email is one of the most economical and fast communication means in recent years; however, there has been a high increase in the rate of spam emails in recent times due to the increased number of email users. Emails are mainly classified into spam and non-spam categories using data mining classification techniques. This paper provides a description and comparative for the evaluation of effective classifiers using three algorithms - namely k-nearest neighbor, Naive Bayesian, and support vector machine. Seven spam email datasets were used to conducted experiment in the MATLAB environment without using any feature selection method. The simulation results showed SVM classifier to achieve a better classification accuracy compared to the K-NN and NB.
Comparison of Algorithms on Machine Learning For Spam Email Classification
IJISTECH (International Journal of Information System and Technology), 2021
The rapid development of email use and the convenience provided make email as the most frequently used means of communication. Along with its development, many parties are abusing the use of email as a means of advertising promotion, phishing and sending other unimportant emails. This information is called spam email. One of the efforts in overcoming the problem of spam emails is by filtering techniques based on the content of the email. In the first study related to the classification of spam emails, the Naïve Bayes method is the most commonly used method. Therefore, in this study researchers will add Random Forest and K-Nearest Neighbor (KNN) methods to make comparisons in order to find which methods have better accuracy in classifying spam emails. Based on the results of the trial, the application of Naïve bayes classification algorithm in the classification of spam emails resulted in accuracy of 83.5%, Random Forest 83.5% and KNN 82.75%
Analysis of Spam Messages Using Various Machine Learning Classifier
Background: As people using social media increases the data generation also increases and the data generated may be safe or unsafe. If we see some applications like Twitter and mail. We get a lot of emails or twits that include all dangerous and useful things. Here to be safe from the threats and dangers we need a filter that separates useful messages from spam and helps us not to drown in a trap. And one of the approaches to do this is explained in this paper. In this paper, the algorithm followed is the Naïve Bayes classifier. This also provides the comparison between using Naïve Bayes, KNN, and Logistic Regression to solve the same problem that is spam filtering and term frequency-inverse document frequency (TFIDF).
Comparative between optimization feature selection by using classifiers algorithms on spam email
International Journal of Electrical and Computer Engineering (IJECE)
Spam mail has become a rising phenomenon in a world that has recently witnessed high growth in the volume of emails. This indicates the need to develop an effective spam filter. At the present time, Classification algorithms for text mining are used for the classification of emails. This paper provides a description and evaluation of the effectiveness of three popular classifiers using optimization feature selections, such as Genetic algorithm, Harmony search, practical swarm optimization, and simulating annealing. The research focuses on a comparison of the effect of classifiers using K-nearest Neighbor (KNN), Naïve Bayesian (NB), and Support Vector Machine (SVM) on spam classifiers (without using feature selection) also enhances the reliability of feature selection by proposing optimization feature selection to reduce number of features that are not important.
Analysis of Random Forest and Naïve Bayes for Spam Mail using Feature Selection Catagorization
International Journal of Computer Applications, 2013
Today, internet users are increases Spam mail is the major problem and big challenges for researcher to reduce it .Spam is commonly defined as unsolicited email messages and the goal of spam categorization is to distinguish between spam and legitimate email messages. This paper shows classification of spam mail and solving various problems is related to web space. Many machine learning algorithm are used to classified the spam and legitimate mail. This paper identify the best classification approach using bench mark dataset .The dataset consist of 9324 records and 500 attributes used for (training and testing) to build the model. This paper can play significant role to help eliminate unsolicited commercial e-mail, viruses, Trojans, and worms, as well as frauds perpetrated electronically and other undesired and troublesome e-mail. Three machines learning supervised algorithms namely naive bayes, Random Tree and Random Forest have applied on spam mail dataset using two feature selection algorithms.
Spam Mails Filtering Using Different Classifiers with Feature Selection and Reduction Technique
2015 Fifth International Conference on Communication Systems and Network Technologies, 2015
The continuous growth of email users has resulted in the increasing of unsolicited emails also known as Spam. In current, server side and client side anti spam filters are introduced for detecting different features of spam emails. However, recently spammers introduced some effective tricks consisting of embedding spam contents into digital image, pdf and doc as attachment which can make ineffective to current techniques that is based on analysis digital text in the body and subject fields of email. Many of proposed working strategy provides an anti spam filtering approach that is based on data mining techniques which classify the spam and ham emails. The effectiveness of these approaches is evaluated on large corpus of simple text dataset as well as text embedded image dataset. But most of the filtering techniques are unable to handle frequent changing scenario of spam mails adopted by the spammers over the time. Therefore improved spam control algorithms or enhancing the efficiency of various existing data mining algorithms to its fullest extent are the utmost requirement. A comparative study is presented on various spam filtering techniques adopted on the basis of various attributes to find best among all to extract the best results.
Spam Mail Detection through Data Mining – A Comparative Performance Analysis
International Journal of Modern Education and Computer Science, 2013
As web is expanding day by day and people generally rely on web for communication so e-mails are the fastest way to send information from one place to another. Now a day's all the transactions all the communication whether general or of business taking place through e-mails. E-mail is an effective tool for communication as it saves a lot of time and cost. But emails are also affected by attacks which include Spam Mails. Spam is the use of electronic messaging systems to send bulk data. Spam is flooding the Internet with many copies of the same message, in an attempt to force the message on people who would not otherwise choose to receive it. In this study, we analyze various data mining approach to spam dataset in order to find out the best classifier for email classification. In this paper we analyze the performance of various classifiers with feature selection algorithm and without feature selection algorithm. Initially we experiment with the entire dataset without selecting the features and apply classifiers one by one and check the results. Then we apply Best-First feature selection algorithm in order to select the desired features and then apply various classifiers for classification. In this study it has been found that results are improved in terms of accuracy when we embed feature selection process in the experiment. Finally we found Random Tree as best classifier for spam mail classification with accuracy = 99.72%. Still none of the algorithm achieves 100% accuracy in classifying spam emails but Random Tree is very nearby to that.
The majority of previous studies of data mining have been concentrate on structured data, such as relational, transactional and data warehouse data. But, in actuality, an important section of the available information is stored in text databases, which consist of large collections of web documents from various sources, such as news articles, research papers, e-books, digital libraries, e-mails, and Web pages. Moreover, It is in increasing phase and in magnitude of terabytes of size. Among the ample of provisions of internet, e-mail facility is very useful and broadly used. Spam email is the strongly attached issue with email provision. Among various approaches developed to stop spam emails, filtering is an important and popular one. In this paper, to categorize spam and non-span email which arrives to our email id, classification method-KNNC Classification can work for better accuracy using Vector Space Model in adaptive manner. For getting accuracy in spam classification we have used two dataset-personal & Ling Spam Corpus(Lemm dataset) and apply KNNC Classification on them. We got nearly 95% of precision in spam & 86.6% of precision in nonspam and got 83% of accuracy using personal dataset and 80% using Lemm dataset using adaptive approach. We propose our own solution by reviewing the result and related work that adaptive approach using vector space model in KNNC classification method is efficiently provide better accuracy for filtering the spam mail for both smaller and larger dataset.