Spam classification: a comparative analysis of different boosted decision tree approaches

Trivedi, Shrawan Kumar; Panigrahi, Prabin Kumar

doi:10.1108/jsit-11-2017-0105

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…The regression tree was used in this study because it contained continuous values in the leaf nodes. These values in the study indicate user interest in the range of 1–5 (Nithya and Santhi, 2015; Trivedi and Panigrahi, 2018). SVR is a regression variant of SVM that is used for continuous value prediction (Fan et al , 2020).…”

Section: The Proposed Methodsmentioning

confidence: 86%

Modeling user preferences in online stores based on user mouse behavior on page elements

SadighZadeh

Kaedi

2022

JSIT

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Purpose Online businesses require a deep understanding of their customers’ interests to innovate and develop new products and services. Users, on the other hand, rarely express their interests explicitly. The purpose of this study is to predict users’ implicit interest in products of an online store based on their mouse behavior through various product page elements. Design/methodology/approach First, user mouse behavior data is collected throughout an online store website. Next, several mouse behavioral features on the product pages elements are extracted and finally, several models are extracted using machine learning techniques to predict a user’s interest in a product. Findings The results indicate that focusing on mouse behavior on various page elements improves user preference prediction accuracy compared to other available methods. Research limitations/implications User mouse behavior was used to predict consumer preferences in this study, therefore gathering additional data on user demography, personality dimensions and emotions may significantly aid in accurate prediction. Originality/value Mouse behavior is the most repeated behavior during Web page browsing through personal computers and laptops. It has been referred to as implicit feedback in some studies and an effective way to ascertain user preference. In these studies, mouse behavior is only assessed throughout the entire Web page, lacking a focus on different page elements. It is assumed that in online stores, user interaction with key elements of a product page, such as an image gallery, user reviews, a description and features and specifications, can be highly informative and aid in determining the user’s interest in that product.

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Section: The Proposed Methodsmentioning

confidence: 86%

Modeling user preferences in online stores based on user mouse behavior on page elements

SadighZadeh

Kaedi

2022

JSIT

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“…However, there is a lack of benchmark datasets for spam detection. Authors in [295] provided a comparative study of various decision tree classifiers such as AD Tree, Decision Stump, and REP Tree.…”

Section: ) Techniques and Methodsmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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“…It is possible to apply the supervised learning algorithms of the classification training model to a set of respective problem states to overcome the problems encountered in the TC. ese models can then be used to identify the unlabeled document class [2,[6][7][8][9][10].…”

Section: Text Classificationmentioning

confidence: 99%

“…e importance of using technologies for classification has increased due to the need to have the ability to automatically classify the huge amounts of diverse text-based information that can be found on the Internet and in electronic/digital format in many languages, including Arabic. Hence, several studies initially focused on addressing the challenges associated with standard Arabic document classifiers [6,7,9,16], which then encouraged more studies that concentrated on enhancing the performance of Arabic document classifiers.…”

Section: Motivation and Objectivesmentioning

confidence: 99%

The Use of Stemming in the Arabic Text and Its Impact on the Accuracy of Classification

Atwan

Wedyan

Bsoul

et al. 2021

Scientific Programming

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The ongoing growth in the vast amount of digital documents and other data in the Arabic language available online has increased the need for classification methods that can deal with the complex nature of such data. The classification of Arabic plays a large and important role in many modern applications and interferes with other sciences, which start from search engines and do not end with the Internet of Things. However, addressing the Arab classification errors with high performance is largely insufficient to deal with the huge quantities to reveal the classification of Arab documents; while some work was tackled out on the classification of the Arabic text, most of the research has focused on English text. The methods proposed for English are not suitable for Arabic as the morphology of the two languages differs substantially. Moreover, morphologically, the preprocessing of Arabic text is a particularly challenging task. In this study, three commonly used classification algorithms, namely, the K-nearest neighbor, Naïve Bayes, and decision tree, were implemented for Arabic text in order to assess their effectiveness with and without the use of a light stemmer in the preprocessing phase. In the experiment, a dataset from Agency France Persse (AFP) Arabic Newswire 2001 consisting of four categories and 800 files was classified using the three classifiers. The result showed that the decision tree with light stemmer had the best accuracy rate for classification algorithm with 93%.

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Spam classification: a comparative analysis of different boosted decision tree approaches

Cited by 15 publications

References 37 publications

Modeling user preferences in online stores based on user mouse behavior on page elements

Modeling user preferences in online stores based on user mouse behavior on page elements

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

The Use of Stemming in the Arabic Text and Its Impact on the Accuracy of Classification

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