333 research outputs found

    Stacking classifiers for anti-spam filtering of e-mail

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    We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or "spam", floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications

    A COMPARISON OF MACHINE LEARNING TECHNIQUES: E-MAIL SPAM FILTERING FROM COMBINED SWAHILI AND ENGLISH EMAIL MESSAGES

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    The speed of technology change is faster now compared to the past ten to fifteen years. It changes the way people live and force them to use the latest devices to match with the speed. In communication perspectives nowadays, use of electronic mail (e-mail) for people who want to communicate with friends, companies or even the universities cannot be avoided. This makes it to be the most targeted by the spammer and hackers and other bad people who want to get the benefit by sending spam emails. The report shows that the amount of emails sent through the internet in a day can be more than 10 billion among these 45% are spams. The amount is not constant as sometimes it goes higher than what is noted here. This indicates clearly the magnitude of the problem and calls for the need for more efforts to be applied to reduce this amount and also minimize the effects from the spam messages. Various measures have been taken to eliminate this problem. Once people used social methods, that is legislative means of control and now they are using technological methods which are more effective and timely in catching spams as these work by analyzing the messages content. In this paper we compare the performance of machine learning algorithms by doing the experiment for testing English language dataset, Swahili language dataset individual and combined two dataset to form one, and results from combined dataset compared them with the Gmail classifier. The classifiers which the researcher used are Naïve Bayes (NB), Sequential Minimal Optimization (SMO) and k-Nearest Neighbour (k-NN). The results for combined dataset shows that SMO classifier lead the others by achieve 98.60% of accuracy, followed by k-NN classifier which has 97.20% accuracy, and Naïve Bayes classifier has 92.89% accuracy. From this result the researcher concludes that SMO classifier can work better in dataset that combined English and Swahili languages. In English dataset shows that SMO classifier leads other algorism, it achieved 97.51% of accuracy, followed by k-NN with average accuracy of 93.52% and the last but also good accuracy is Naïve Bayes that come with 87.78%. Swahili dataset Naïve Bayes lead others by getting 99.12% accuracy followed by SMO which has 98.69% and the last was k-NN which has 98.47%

    SDRS: a new lossless dimensionality reduction for text corpora

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    In recent years, most content-based spam filters have been implemented using Machine Learning (ML) approaches by means of token-based representations of textual contents. After introducing multiple performance enhancements, the impact has been virtually irrelevant. Recent studies have introduced synset-based content representations as a reliable way to improve classification, as well as different forms to take advantage of semantic information to address problems, such as dimensionality reduction. These preliminary solutions present some limitations and enforce simplifications that must be gradually redefined in order to obtain significant improvements in spam content filtering. This study addresses the problem of feature reduction by introducing a new semantic-based proposal (SDRS) that avoids losing knowledge (lossless). Synset-features can be semantically grouped by taking advantage of taxonomic relations (mainly hypernyms) provided by BabelNet ontological dictionary (e.g. “Viagra” and “Cialis” can be summarized into the single features “anti-impotence drug”, “drug” or “chemical substance” depending on the generalization of 1, 2 or 3 levels). In order to decide how many levels should be used to generalize each synset of a dataset, our proposal takes advantage of Multi-Objective Evolutionary Algorithms (MOEA) and particularly, of the Non-dominated Sorting Genetic Algorithm (NSGA-II). We have compared the performance achieved by a Naïve Bayes classifier, using both token-based and synset-based dataset representations, with and without executing dimensional reductions. As a result, our lossless semantic reduction strategy was able to find optimal semantic-based feature grouping strategies for the input texts, leading to a better performance of Naïve Bayes classifiers.info:eu-repo/semantics/acceptedVersio

    Survey of review spam detection using machine learning techniques

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    Improving spam email classification accuracy using ensemble techniques: a stacking approach

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    Spam emails pose a substantial cybersecurity danger, necessitating accurate classification to reduce unwanted messages and mitigate risks. This study focuses on enhancing spam email classification accuracy using stacking ensemble machine learning techniques.We trained and tested five classifiers: logistic regression, decision tree, K-nearest neighbors (KNN), Gaussian naive Bayes and AdaBoost. To address overfitting, two distinct datasets of spam emails were aggregated and balanced. Evaluating individual classifiers based on recall, precision and F1 score metrics revealed AdaBoost as the top performer. Considering evolving spam technology and new message types challenging traditional approaches, we propose a stacking method. By combining predictions from multiple base models, the stacking method aims to improve classification accuracy. The results demonstrate superior performance of the stacking method with the highest accuracy (98.8%), recall (98.8%) and F1 score (98.9%) among tested methods. Additional experiments validated our approach by varying dataset sizes and testing different classifier combinations. Our study presents an innovative combination of classifiers that significantly improves accuracy, contributing to the growing body of research on stacking techniques. Moreover, we compare classifier performances using a unique combination of two datasets, highlighting the potential of ensemble techniques, specifically stacking, in enhancing spam email classification accuracy. The implications extend beyond spam classification systems, offering insights applicable to other classification tasks. Continued research on emerging spam techniques is vital to ensure long-term effectiveness

    Comparative Study of Gaussian and Nearest Mean Classifiers for Filtering Spam E-mails

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    The development of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. The article gives an overview of some of the most popular machine learning methods (Gaussian and Nearest Mean) and of their applicability to the problem of spam e-mail filtering. The aim of this paper is to compare and investigate the effectiveness of classifiers for filtering spam e-mails using different matrices. Since spam is increasingly becoming difficult to detect, so these automated techniques will help in saving lot of time and resources required to handle e-mail messages

    COMPARISON OF MACHINE LEARNING TECHNIQUES IN SPAM E-MAIL CLASSIFICATION

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    E-mail still proves to be very popular and an efficient communication tool. Due to its misuse, however, managing e-mails is an important problem for organizations and individuals. Spam, known as unwanted message, is an example of misuse. Specifically, spam is defined as the arrival of unwelcomed bulk email not being requested for by recipients. This paper compares different Machine Learning Techniques in classification of spam e-mails. Random Forest (RF), C4.5 decision tree and Artificial Neural Network (ANN) were tested to determine which method provides the best results in spam e-mail classification. Our results show that RF is the best technique applied on dataset from HP Labs, indicating that ensemble methods may have an edge in spam detectio
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