Web Spam Detection Using Link-Based Ant Colony Optimization

Taweesiriwate, Apichat; Manaskasemsak, Bundit; Rungsawang, Arnon

doi:10.1109/aina.2012.118

Cited by 17 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Gray Hat SEO is a transformation from White to Black and from Black to White. Usually, most of the companies are practicing the Gray Hat techniques to some extent for Search Engine Optimization due to the pressure from website owners to deliver excellent and quick results [64]. Moreover, they are not crossing the line to Black Hat SEO.…”

Section: Gray Hat Search Engine Optimizationmentioning

confidence: 99%

The new trend for search engine optimization, tools and techniques

Shahzad

Jacob

Nawi

et al. 2020

IJEECS

View full text Add to dashboard Cite

<span>Search Engines are used to search any information on the internet. <br /> The primary objective of any website owner is to list their website at the top of all the results in Search Engine Results Pages (SERPs). Search Engine Optimization is the art of increasing visibility of a website in Search Engine Result Pages. This art of improving the visibility of website requires the tools and techniques; This paper is a comprehensive survey of how a Search Engine (SE) works, types and parts of Search Engine and different techniques and tools used for Search Engine Optimization (SEO.) In this paper, we will discuss the current tools and techniques in practice for Search Engine Optimization.</span>

show abstract

Section: Gray Hat Search Engine Optimizationmentioning

confidence: 99%

The new trend for search engine optimization, tools and techniques

Shahzad

Jacob

Nawi

et al. 2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…Link analysis is done by Apichat et al [3] using ant colony optimization in order to classify spam pages created using link spamming. Here the host graph is constructed by aggregating hyperlink structure of pages and ant starts walking from a normal host and randomly follows host links with probability distribution of TrustRank assumption.Yutak et.…”

Section: Related Workmentioning

confidence: 99%

“…But one variant is generated from an empty rule while the other is generated by greedily adding antecedents to the original rule. Moreover, the pruning metric used here is (3) Then the smallest possible DL for each variant and the original rule is computed. The variant with the minimal DL is selected as the final representative of Ri in the ruleset.…”

Section: Optimization Stagementioning

confidence: 99%

Comparative Study of Web Spam Detection using Data Mining

Nathwani¹,

Prajapati²,

Agravat³

2013

IJCA

View full text Add to dashboard Cite

Today World Wide Web has become one of best sources of information which is result of faster working of search engines. Web spam attempts to sway search engine algorithm in order to boost the page ranking of specific web pages in search engine results than they deserve. One way to detect web spam is using classification that is learning a classification model for classifying web pages to spam or nonspam. Comparative and empirical analysis of web spam detection using data mining techniques like LAD Tree, JRIP, J48 and Random Forest have been presented in this paper. Experiments were carried out on 3 feature sets of standard dataset WEB SPAM UK-2007. Overall results say that Random forest works well with content based features and transformed link based features however LAD tree was found best among 4 in link based features. But, while thinking about time efficiency LAD Tree was found much more time consuming as compare other 3 classification techniques.

show abstract

“…Abernethy simultaneously exploits the structure of the Web graph as well as page content features for web spam detection [9]. Taweesiriwate present a link-based ant colony optimization learning algorithm for spam host detection [10]. Following the TrustRank assumption, ants start walking from a normal host and randomly follow host links with a probability distribution.…”

Section: Introductionmentioning

confidence: 99%

Web spam detection based on improved tri-training

2014

2014 IEEE International Conference on Progress in Informatics and Computing

View full text Add to dashboard Cite

Web spamming is the deliberate manipulation of search engine indexes to make a page get high ranking than which it deserved considering its true value. Since the evolution of web spam, a new based on machine learning algorithm web spam detection method which has self-learning ability has emerged. Web spam detection is viewed as a binary classification learning problem. Because labeled training examples are fairly expensive to obtain which need the participation of experts in this field and labor costs, how to fully utilize a large number of unlabeled web page examples on the web is a challenge faced by web spam detection. In this paper, we present a web spam detection algorithm according to improve tri-training. It uses a small amount of labeled examples and a large number of unlabeled examples to train classifiers, which can reduce the cost of labeled examples and improve the learning performance. Both web page content features and link features are used in this paper.

show abstract

Web Spam Detection Using Link-Based Ant Colony Optimization

Cited by 17 publications

References 15 publications

The new trend for search engine optimization, tools and techniques

The new trend for search engine optimization, tools and techniques

Comparative Study of Web Spam Detection using Data Mining

Web spam detection based on improved tri-training

Contact Info

Product

Resources

About