An effective feature selection method for web spam detection

Asdaghi, Faeze; Soleimani, Ali

doi:10.1016/j.knosys.2018.12.026

Cited by 31 publications

(8 citation statements)

References 24 publications

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“…e author used the modified knuth-morris-pratt algorithm to extract features from Alexa Top 500 Global Sites and Bing search engine results in 500 queries; then, they generated a tree model with useful attributes that can detect web spam. Asdaghi and Soleimani [23] proposed a new backward elimination feature selection approach with the Naive Bayes (NB) classifier.…”

Section: Related Workmentioning

confidence: 99%

“…en we select features from the 106 features. We use a new backward elimination approach, Smart-BT, proposed by Asdaghi and Soleimani [23] to accomplish feature selection. is method differs from sequence backward elimination in that it measures the impact on the classification result after eliminating a set of features, rather than eliminating a single feature.…”

Section: Precomputed Featuresmentioning

confidence: 99%

“…Rate. We also have compared some state-of-art methods; for example, Mittal and Juneja [46] presented a mutual informationbased feature selection method which selects content-based features and with a SVM classifier to distinguish web spam, Makkar et al [26] used PCA and RFE to deal with link-based features and incorporated the features into an RNN classifier to detect spam, and Asdaghi and Soleimani [23] proposed an effective feature selection method to select fewer features and put the features into NB model to achieve high performance. We used the same method as these papers to divide the dataset into training, validation, and test sets.…”

Section: Experiments For Comparison Of Detectionmentioning

confidence: 99%

See 2 more Smart Citations

Detecting Web Spam Based on Novel Features from Web Page Source Code

Liu

et al. 2020

Security and Communication Networks

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Search engine is critical in people’s daily life because it determines the information quality people obtain through searching. Fierce competition for the ranking in search engines is not conducive to both users and search engines. Existing research mainly studies the content and links of websites. However, none of these techniques focused on semantic analysis of link and anchor text for detection. In this paper, we propose a web spam detection method by extracting novel feature sets from the homepage source code and choosing the random forest (RF) as the classifier. The novel feature sets are extracted from the homepage’s links, hypertext markup language (HTML) structure, and semantic similarity of content. We conduct experiments on the WEBSPAM-UK2007 and UK-2011 dataset using a five-fold cross-validation method. Besides, we design three sets of experiments to evaluate the performance of the proposed method. The proposed method with novel feature sets is compared with different indicators and has better performance than other methods with a precision of 0.929 and a recall of 0.930. Experiment results show that the proposed model could effectively detect web spam.

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Section: Related Workmentioning

confidence: 99%

Section: Precomputed Featuresmentioning

confidence: 99%

Section: Experiments For Comparison Of Detectionmentioning

confidence: 99%

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Detecting Web Spam Based on Novel Features from Web Page Source Code

Liu

et al. 2020

Security and Communication Networks

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“…Several approaches have been proposed for spam detection [8]. To evaluate the performance of the filters, it has been published diverse corpus [9], different measures [10] and evaluation methods [11] have been used.…”

Section: Introductionmentioning

confidence: 99%

Goal-oriented Email Stream Classifier with A Multi-agent System Approach

Hojas-Mazo¹,

Espino²,

Martínez³

et al. 2021

IJACSA

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Now-a-days, email is often one of the most widely used means of communication despite the rise of other communication methods such as instant messaging or communication via social networks. The need to automate the email stream management increases for reasons such as multifolder categorization, and spam email classification. There are solutions based on email content, capable of contemplating elements such as the text subjective nature, adverse effects of concept drift, among others. This paper presents an email stream classifier with a goal-oriented approach to client and server environment. The i* language was the basis for designing the proposed email stream classifier. The email environment was represented with the early requirements model and the proposed classifier with the late requirements model. The classifier was implemented following a multi-agent system approach supported by JADE agent platform and Implementation_JADE pattern. The behavior of agents was taking from an existing classifier.The multi-agent classifier was evaluated using functional, efficacy and performance tests, which compared the existing classifier with the multi-agent approach. The results obtained were satisfactory in all the tests. The performance of multi-agent approach was better than the existing classifier due to the use of multi-threads.

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“…The feature extraction stages generally utilize the vector space model (Salton et al, 1975) that makes use of the bag-of-words approach (Joachims, 1997). Finally, the feature selection stage typically uses the filter method such as document frequency (Azam and Yao, 2012;Yang and Pedersen, 1997), mutual information (Tang et al, 2019;Al-Angari et al, 2016;Liu et al, 2009), information gain (Mendez et al, 2019;Lee and Lee, 2006), chi-square (Asdaghi and Soleimani, 2019;Chen and Chen, 2011) and Odds Ratio (Raza and Qamar, 2016;Feng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Review of feature extraction approaches on biomedical text classification

al.¹

2020

Int. j. adv. appl. sci.

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The overcoming volume of online biomedical literature causes congestion of data and difficulties in organizing these documents and also to retrieve the required documents from the database, especially in the Medline database. One of the solutions to surpass the overwhelming of documents is to apply classification. However, each document must be represented by a set of terminology or feature vectors. The identification of terminology or feature from biomedical literature is one of the most important and challenging tasks in text classification. This is due to a large number of new features and entities that appear in the biomedical domain. In addition, combining sets of features from different terminological resources leads to naming conflicts such as homonymous use of names and terminological ambiguities. Therefore, the purpose of this research is to investigate and evaluate the effective ways for extracting the relevant and meaningful features in order to increase the classification accuracy and improve the performance of web searches. Towards this effort, we conduct several classification experiments to evaluate and compare the effectiveness of feature extraction approaches for extracting the relevant and informative features from the biomedical literature. For our experiments, we use two different sets of features, which are a set of features that are extracted using the Genia tagger tool and set of features that are extracted by medical experts from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM). The results show the performance of classification using features that are extracted by medical experts outperform the performance of classification using the Genia Tagger tool when applying feature selection method.

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An effective feature selection method for web spam detection

Cited by 31 publications

References 24 publications

Detecting Web Spam Based on Novel Features from Web Page Source Code

Detecting Web Spam Based on Novel Features from Web Page Source Code

Goal-oriented Email Stream Classifier with A Multi-agent System Approach

Review of feature extraction approaches on biomedical text classification

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