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Ensemble Decision for Spam Detection Using Term Space Partition Approach

Ying Tan, Quanbin Wang, Guyue Mi
2018 IEEE Transactions on Cybernetics  
This paper proposes an ensemble decision approach which combines global and local features of e-mails together to detect spam effectively.  ...  In the proposed method, a special feature construction method named term space partition (TSP) is utilized to divide the whole term space into several subspaces and adopt different feature construction  ...  ENSEMBLE DECISION USING TSP APPROACH A.  ... 
doi:10.1109/tcyb.2018.2868794 pmid:30273168 fatcat:jqre5hgrnbbl3ovhvurwhe7axe

A Feature-Partition and Under-Sampling Based Ensemble Classifier for Web Spam Detection

Xiaoyong Lu, Musheng Chen, Jhenglong Wu, Peichan Chan
2015 International Journal of Machine Learning and Computing  
Index Terms-Web spam detection, under-sampling, features partition, ensemble classifier, C4.5.  ...  Web spam detection has become one of the top important tasks for web search engines. Web spam detection is a class imbalance problem because normal pages are far more than spam pages.  ...  ACKNOWLEDGMENT The authors wish to acknowledge Carlos Castillo, who has supported the WEBSPAM-UK2006 Corpus web site and helped us to download the collection.  ... 
doi:10.18178/ijmlc.2015.5.6.551 fatcat:ytkh6zymenc3hdlotehzaytyza

Development of Proposed Ensemble Model for Spam e-mail Classification

Akhilesh Kumar Shrivas, Amit Kumar Dewangan, S M Ghosh, Devendra Singh
2021 Information Technology and Control  
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.  ...  results reveal that the proposed Ensemble Model-1 outperforms other existing classifiers aswell as other proposed ensemble models in terms of classification accuracy.  ...  [32] suggested and used text semantic analysis to improve the performance of model for spam detection.  ... 
doi:10.5755/j01.itc.50.3.27349 fatcat:4wtu3ujwszckriexu2bkfedeee

A lifelong spam emails classification model

Rami Mustafa A. Mohammad
2020 Applied Computing and Informatics  
Spam emails classification using data mining and machine learning approaches has enticed the researchers' attention duo to its obvious positive impact in protecting internet users.  ...  Several features can be used for creating data mining and machine learning based spam classification models.  ...  These results confirm that the ensemble-based spam classification approach is more suitable for identifying spam emails.  ... 
doi:10.1016/j.aci.2020.01.002 fatcat:biwpczx3zveshf2ug4dmjz7iiy

Fake Job Detection Using Machine Learning

Priya Khandagale, Akshata Utekar, Anushka Dhonde, Prof. S. S. Karve
2022 International Journal for Research in Applied Science and Engineering Technology  
In order to detect bogus posts, a machine learning approach is used, which employs numerous categorization algorithms.  ...  Keywords: Fake Job, Online Recruitment, Machine Learning, Ensemble Approach.  ...  To partition the data points, SVM constructs a hyper level in authentic input space.  ... 
doi:10.22214/ijraset.2022.41641 fatcat:r7k4fea5mreqtk575gnn5up3ce

An Ensemble Model for Identification of Phishing Website

Jaspreet Kaur Virdi
2017 International Journal for Research in Applied Science and Engineering Technology  
In this research work , we have used many data mining based classification techniques like C4.5, SimpleCart, Random tree, SVM and MLP for classification of phishing websites with different data partitions  ...  We have achieved better accuracy with ensemble of C4.5, SimpleCart , MLP and Random tree with all data partitions, but it achieved best accuracy as 97.16% in case of 85-15% data partition.  ...  Shrivas et al. (2015) [10] have used various decision tree based classification techniques and its ensemble model for classification of spam and phishing e-mail data.  ... 
doi:10.22214/ijraset.2017.4205 fatcat:2wmoczrdsrdntlyhcaolqyf5yi

A Robust System for Message Filtering Using an Ensemble Machine Learning Supervised Approach

Atik Mahabub, Mohammed Innat Mahmud, Md Faruque Hossain
2019 Innovative Computing Information and Control Express Letters, Part B: Applications  
In this paper, based on Ensemble Voting Classifier, an intelligent detection system based on EVC is proposed to deal with Email detection on both ham and spam cases.  ...  Here, eleven mostly well-known machine-learning algorithms like Naïve Bayes, K-NN, SVC, Random Forest, Artificial Neural Network, Logistic Regression, Gradient Boosting, and Ada Boosting are used for detection  ...  In this article, a novel multi-classifier based Ensemble Voting Classifier technique is proposed for detecting both spam and non-spam messages.  ... 
doi:10.24507/icicelb.10.09.805 fatcat:kwgnf7w7m5eg7nsfe3djswomwy

Unsupervised Approach for Email Spam Filtering using Data Mining

Mehdi Manaa, Ahmed Obaid, Mohammed Dosh
2018 EAI Endorsed Transactions on Energy Web  
These N-representative points are formed from the training step to detect spam email using distance measures. The data set used from the Kaggle website included many objects of ham and spam emails.  ...  In this paper, the increasing volume of these emails has created the intense need to design and implement robust anti-spam filtering using the vector space model and Machine Learning (ML).  ...  The Proposed System Currently, there is an increasing concern in data mining approaches for spam emails detection.  ... 
doi:10.4108/eai.9-3-2021.168962 fatcat:5s7idhq2kfdxjax7ew5gaklb3y

Clustering Ensemble for Spam Filtering [chapter]

Santiago Porras, Bruno Baruque, Belén Vaquerizo, Emilio Corchado
2011 Lecture Notes in Computer Science  
The system divides the spam ltering problem into two stages: rstly it divides the input data space into dierent similar parts.  ...  This study presents a novel hybrid intelligent system using both unsupervised and supervised learning that can be easily adapted to be used in an individual or collaborative system.  ...  As explained in this work, one interesting topic on spam-ltering is the collaborative approaches, where several ltering systems work together to detect spam.  ... 
doi:10.1007/978-3-642-21222-2_44 fatcat:5lcrvdwygrcwbc4pnfjfzgblw4

Multi-View Learning for Web Spam Detection [article]

Ali Hadian, Behrouz Minaei-Bidgoli
2013 arXiv   pre-print
Previous methods for web spam classification used several features from various information sources (page contents, web graph, access logs, etc.) to detect web spam.  ...  Therefore, spam pages should be removed using an effective and efficient spam detection system.  ...  They also wish to thank Hadi Sharifi, Amin Nikookaran, and Ali Shirvani for their collaboration.  ... 
arXiv:1305.3814v2 fatcat:t7b6osiq6zezdoeuch7uqydkyu

Multi-View Learning for Web Spam Detection

Ali Hadian, Behrouz Minaei-Bidgoli
2013 Journal of Emerging Technologies in Web Intelligence  
Previous methods for web spam classification used several features from various information sources (page contents, web graph, access logs, etc.) to detect web spam.  ...  Therefore, spam pages should be removed using an effective and efficient spam detection system.  ...  They also wish to thank Hadi Sharifi, Amin Nikookaran, and Ali Shirvani for their collaboration.  ... 
doi:10.4304/jetwi.5.4.395-400 fatcat:omyaxazwurerhc427ghphnoupi

Machine Learning Techniques for Spam Detection in Email and IoT Platforms: Analysis and Research Challenges

Naeem Ahmed, Rashid Amin, Hamza Aldabbas, Deepika Koundal, Bader Alouffi, Tariq Shah
2022 Security and Communication Networks  
Among all the techniques developed for detecting and preventing spam, filtering email is one of the most essential and prominent approaches.  ...  Several machine learning and deep learning techniques have been used for this purpose, i.e., Naïve Bayes, decision trees, neural networks, and random forest.  ...  Sasaki and Shinnou [82] introduce a new approach for spam detection using the vector-space model of content clustering. eir system automatically calculates disjoint clusters using a spherical k-means  ... 
doi:10.1155/2022/1862888 doaj:27c7fab9297747cba125d6dff8deb631 fatcat:fsasq7gapbhd7lv66wwgxveyyq

A on Spam Filtering Classification: A Majority Voting like Approach

Youngsu Dong, Mourad Oussalah, Lauri Lovén
2017 Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
In this paper, we present a majority-voting based approach in order to identify spam messages. A new methodology for building majority voting classifier is presented and tested.  ...  A on Spam Filtering Classification: A Majority Voting like Approach.  ...  This paper focuses on the first class, namely, the content-based approach. We propose a new method using an ensemble of classifiers. A classifier-based approach for spam-detection is nothing new.  ... 
doi:10.5220/0006581102930301 dblp:conf/ic3k/DongOL17 fatcat:vhm7ai3yqbdvxlphqpstowz3iq

Comparative Analysis of Different Techniques for Novel Class Detection

Patel Jignasa N, Sheetal Mehta
2012 International Journal of Computer Applications Technology and Research  
Figure 2 : 2 (a) A decision tree, (b) corresponding feature space partitioning where FS(X) denotes the Feature space defined by a leaf node X The shaded areas show the used spaces of each partition.  ...  In [1] authors have proposed New decision tree learning approach for detection of Novel class.  ... 
doi:10.7753/ijcatr0103.1002 fatcat:bq2ndicybfalzbefo2o4qsoeo4

Multi-classifier Classification of Spam Email on a Ubiquitous Multi-core Architecture

Rafiqul Islam, Jaipal Singh, Ashley Chonka, Wanlei Zhou
2008 2008 IFIP International Conference on Network and Parallel Computing  
Many approaches use text-based single classifiers or multiple weakly trained classifiers to identify spam messages from a large email corpus.  ...  We also reduced the instance of false positive, which is one of the key challenges in spam filtering system, and increases email classification accuracy substantially compared with single classification  ...  Several other ensemble techniques for spam detection include NB bagging [18] and semi-supervised labelled messages [7] .  ... 
doi:10.1109/npc.2008.71 dblp:conf/npc/IslamSCZ08 fatcat:pufw7ptdz5ft5j3j2ietjpvqzq
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