Automatic Web Page Classification by Combining Feature Selection Techniques and Lazy Learners

2007 ◽  
Author(s):  
M. Indra Devi ◽  
R. Rajaram ◽  
K. Selvakuberan
Keyword(s):  
Feature Selection ◽  
Web Page ◽  
Web Page Classification ◽  
Feature Selection Techniques ◽  
Page Classification

Two novel feature selection approaches for web page classification

2009 ◽  
Vol 36 (1) ◽  
pp. 260-272 ◽  
Author(s):  
Chih-Ming Chen ◽  
Hahn-Ming Lee ◽  
Yu-Jung Chang
Keyword(s):  
Feature Selection ◽  
Web Page ◽  
Web Page Classification ◽  
Page Classification

A novel feature selection framework for automatic web page classification

2012 ◽  
Vol 9 (4) ◽  
pp. 442-448 ◽  
Author(s):  
J. Alamelu Mangai ◽  
V. Santhosh Kumar ◽  
S. Appavu alias Balamurugan
Keyword(s):  
Feature Selection ◽  
Web Page ◽  
Web Page Classification ◽  
Selection Framework ◽  
Page Classification

scholarly journals Research of Novel Web Page Classifiers and Feature Selection Methods

2019 ◽  
Vol 8 (11S) ◽  
pp. 51-55
Keyword(s):  
Feature Selection ◽  
Web Technology ◽  
Selection Methods ◽  
Web Page ◽  
Web Page Classification ◽  
Current Trends ◽  
Web Information ◽  
The World ◽  
The Web ◽  
Page Classification

The World revolves around the web technology at present. Every year, the Web information are exponentially growing and this information are huge and complex. The web users are difficult to classify and extract useful information from the web, because the Webinformation are noisy, redundant and irrelevant and also misclassified.Many researchers don’t have strongknowledge about the process of web page classification, techniques and methods previously used. The objective of this survey is to convey an outline of the modern techniques of Web page classification. In this survey, the recent papers in this area are selected and explored.Thus this study will help the researchers to obtain the required knowledge about the current trends in web page classification

scholarly journals An Ant Colony Optimization Based Feature Selection for Web Page Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Esra Saraç ◽  
Selma Ayşe Özel
Keyword(s):  
Feature Selection ◽  
Ant Colony Optimization ◽  
Information Gain ◽  
Classification Systems ◽  
Ant Colony ◽  
Web Pages ◽  
Web Page ◽  
Web Page Classification ◽  
The Web ◽  
Page Classification

The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines’ performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, andknearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods.

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