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Ensemble Decision for Spam Detection Using Term Space Partition Approach
2018
IEEE Transactions on Cybernetics
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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
2015
International Journal of Machine Learning and Computing
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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
2021
Information Technology and Control
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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. ...
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
2020
Applied Computing and Informatics
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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
2022
International Journal for Research in Applied Science and Engineering Technology
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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
2017
International Journal for Research in Applied Science and Engineering Technology
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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
2019
Innovative Computing Information and Control Express Letters, Part B: Applications
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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
2018
EAI Endorsed Transactions on Energy Web
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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]
2011
Lecture Notes in Computer Science
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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]
2013
arXiv
pre-print
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2013 pre-print arXiv:1305.3814v1 fatcat:vn5tlcrvhrgnxihrtfazfd3s6q
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
2013
Journal of Emerging Technologies in Web Intelligence
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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
2022
Security and Communication Networks
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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
2017
Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
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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
2012
International Journal of Computer Applications Technology and Research
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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
2008
2008 IFIP International Conference on Network and Parallel Computing
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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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