Concept drift in e-mail datasets: An empirical study with practical implications

Ruano-Ordás, David; Fdez-Riverola, Florentino; Méndez, J.

doi:10.1016/j.ins.2017.10.049

Cited by 30 publications

(15 citation statements)

References 25 publications

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“…The calculation details will be introduced as follows. Note that the usage of word stemming (Step 3) would cause a reduction in the performance of classification techniques owing to the generation of over‐steaming and under‐steaming errors [41 ]. The adoption of word ontologies such as WordNet or other alternatives can substitute for the usage of word stemming [42, 43 ].…”

Section: Effective Two‐phase Hybrid Spam Filtering Methods Integratimentioning

confidence: 99%

Effective spam filter based on a hybrid method of header checking and content parsing

Chu¹,

Hsu²,

Sheu

et al. 2020

IET Networks

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In recent years, hazardous e-mails arose, such as the e-mails infected with 'viruses' or 'worms' spreading destructive programs and the 'Phishing Mails' defrauding e-mail accounts of the users. The number of spams continue to grow. With the related problems of spam coming to be more severe, the spam topics have become significant in various research domains. The common filtering methods include black/white list, rule learning, and those based on text classification, such as Naïve Bayes, support vector machine, and boosting trees, multi-agent and genetic algorithm. Among these, the methods based on text classification are most widely applied. Moreover, some efficient methods were proposed to consider only the e-mail's header section, based on which both operating efficiency and classification efficiency could be improved. By applying machine learning technique and decision tree data mining algorithm C4.5, this study aims to propose an efficient spam filtering method with the following features: (i) proposing a two-phase filtering mechanism to scan mainly e-mail's header and auxiliary content. (ii) Reducing the problem of false positive. The experimental results show that the authors' method has a considerably high accuracy rate of 98.76%. Compared with some other methods of using the same spam data sets or of deep learning-based, their method obviously has an excellent performance.

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Section: Effective Two‐phase Hybrid Spam Filtering Methods Integratimentioning

confidence: 99%

Effective spam filter based on a hybrid method of header checking and content parsing

Chu¹,

Hsu²,

Sheu

et al. 2020

IET Networks

View full text Add to dashboard Cite

show abstract

“…Naïve Bayes algorithm [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] is simple and based on probability theorem. It [23] is a statistical classifier also known as the Naive Bayesian classifier that can be used for classification of data.…”

Section: Naïve Bayes (Nb)mentioning

confidence: 99%

“…The performance measures [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] play a very important role in checking the robustness of a model. We have calculated accuracy, sensitivity, specificity, precision, F-score and ROC curve using different parameters of the confusion matrix.…”

Section: Performance Measuresmentioning

confidence: 99%

Development of Proposed Ensemble Model for Spam e-mail Classification

Shrivas¹,

Dewangan²,

Ghosh³

et al. 2021

ITC

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Spam e-mail documents classification is a very challenging task for e-mail users, especially non IT users. Billionsof people using the internet and face the problem of spam e-mails. The automatic identification and classificationof spam e-mails help to reduce the problem of e-mail users in managing a large amount of e-mails. This work aimsto do a significant contribution by building a robust model for classification of spam e-mail documents using datamining techniques. In this paper, we use Enorn1 data set which consists of spam and ham documents collectedfrom Kaggle repository. We propose an Ensemble Model-1 that is an ensemble of Multilayer Perceptron (MLP),Naïve Bayes and Random Forest (RF) to obtain better accuracy for the classification of spam and hame-mail documents.Experimental results reveal that the proposed Ensemble Model-1 outperforms other existing classifiers aswell as other proposed ensemble models in terms of classification accuracy. The suggested and proposed EnsembleModel-1 produces a high accuracy of 97.25% for classification of spam e-mail documents.

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“…The concept drift phenomenon may occur in various real-world data and corresponding applications ( Žliobaite, Pechenizkiy & Gama, 2016 ): computer systems or networks, through network intrusion detection, where new techniques and methods may appear ( Liu et al, 2017 ; Mukkavilli & Shetty, 2012 ); industry, when dynamic data streams are produced by sensors in production equipment and machines ( Lin et al, 2019 ; Zenisek, Holzinger & Affenzeller, 2019 ); marketing and management, when users change their buying behavior and their preferences ( Black & Hickey, 2003 ; Chiang, Wang & Chu, 2013 ; Lo et al, 2018 ); medical data, e.g., in the case of antibiotic resistance ( Stiglic & Kokol, 2011 ; Tsymbal et al, 2006 ); social networks, when users change their behavior and generated content ( Lifna & Vijayalakshmi, 2015 ; Li et al, 2016 ); spam categorization, where spam keywords can change over time ( Delany et al, 2005 ; Ruano-Ordás, Fdez-Riverola & Méndez, 2018 ). …”

Section: Introductionmentioning

confidence: 99%

“…spam categorization, where spam keywords can change over time ( Delany et al, 2005 ; Ruano-Ordás, Fdez-Riverola & Méndez, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble

Sarnovský

Kolárik

2021

PeerJ Computer Science

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Data streams can be defined as the continuous stream of data coming from different sources and in different forms. Streams are often very dynamic, and its underlying structure usually changes over time, which may result to a phenomenon called concept drift. When solving predictive problems using the streaming data, traditional machine learning models trained on historical data may become invalid when such changes occur. Adaptive models equipped with mechanisms to reflect the changes in the data proved to be suitable to handle drifting streams. Adaptive ensemble models represent a popular group of these methods used in classification of drifting data streams. In this paper, we present the heterogeneous adaptive ensemble model for the data streams classification, which utilizes the dynamic class weighting scheme and a mechanism to maintain the diversity of the ensemble members. Our main objective was to design a model consisting of a heterogeneous group of base learners (Naive Bayes, k-NN, Decision trees), with adaptive mechanism which besides the performance of the members also takes into an account the diversity of the ensemble. The model was experimentally evaluated on both real-world and synthetic datasets. We compared the presented model with other existing adaptive ensemble methods, both from the perspective of predictive performance and computational resource requirements.

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Concept drift in e-mail datasets: An empirical study with practical implications

Cited by 30 publications

References 25 publications

Effective spam filter based on a hybrid method of header checking and content parsing

Effective spam filter based on a hybrid method of header checking and content parsing

Development of Proposed Ensemble Model for Spam e-mail Classification

Classification of the drifting data streams using heterogeneous diversified dynamic class-weighted ensemble

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