An Ant Colony Optimization Based Feature Selection for 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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/649260 |
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| _version_ | 1832551501130629120 |
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| author | Esra Saraç Selma Ayşe Özel |
| author_facet | Esra Saraç Selma Ayşe Özel |
| author_sort | Esra Saraç |
| collection | DOAJ |
| description | 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, and k nearest 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. |
| format | Article |
| id | doaj-art-e30930b5a00040f6ac12216a3eb320cd |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-e30930b5a00040f6ac12216a3eb320cd2025-02-03T06:01:20ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/649260649260An Ant Colony Optimization Based Feature Selection for Web Page ClassificationEsra Saraç0Selma Ayşe Özel1Department of Computer Engineering, Çukurova University, Balcali, Sarıçam, 01330 Adana, TurkeyDepartment of Computer Engineering, Çukurova University, Balcali, Sarıçam, 01330 Adana, TurkeyThe 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, and k nearest 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.http://dx.doi.org/10.1155/2014/649260 |
| spellingShingle | Esra Saraç Selma Ayşe Özel An Ant Colony Optimization Based Feature Selection for Web Page Classification The Scientific World Journal |
| title | An Ant Colony Optimization Based Feature Selection for Web Page Classification |
| title_full | An Ant Colony Optimization Based Feature Selection for Web Page Classification |
| title_fullStr | An Ant Colony Optimization Based Feature Selection for Web Page Classification |
| title_full_unstemmed | An Ant Colony Optimization Based Feature Selection for Web Page Classification |
| title_short | An Ant Colony Optimization Based Feature Selection for Web Page Classification |
| title_sort | ant colony optimization based feature selection for web page classification |
| url | http://dx.doi.org/10.1155/2014/649260 |
| work_keys_str_mv | AT esrasarac anantcolonyoptimizationbasedfeatureselectionforwebpageclassification AT selmaayseozel anantcolonyoptimizationbasedfeatureselectionforwebpageclassification AT esrasarac antcolonyoptimizationbasedfeatureselectionforwebpageclassification AT selmaayseozel antcolonyoptimizationbasedfeatureselectionforwebpageclassification |