2008 23rd International Symposium on Computer and Information Sciences, 2008
... Selma Ayşe Özel, Esra Saraç Computer Engineering Department, Çukurova University, Faculty of ... more ... Selma Ayşe Özel, Esra Saraç Computer Engineering Department, Çukurova University, Faculty of Engineering and Architecture 01330 Balcalı, Adana ... Ulusoy, G. Özsoyoğlu, and ZM Özsoyoğlu, “Topic-Centric Querying of Web Information Resources”, Lecture Notes in Computer ...
ABSTRACT Increase in the amount of information on the Web has caused the need for accurate automa... more ABSTRACT Increase in the amount of information on the Web has caused the need for accurate automated classifiers for Web pages to maintain Web directories and to increase search engines' performance. As every (HTML/XML) tag and every term on each Web page can be considered as a feature, we need efficient methods to select best features to reduce feature space of the Web page classification problem. In this study, our aim is to apply a recent optimization technique namely the firefly algorithm (FA), to select best features for Web page classification problem. The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. In this study, we use FA to select a subset of features, and to evaluate the fitness of the selected features J48 classifier of the Weka data mining tool is employed. WebKB and Conference datasets were used to evaluate the effectiveness of the proposed feature selection system. We observed that when a subset of features are selected by using FA, WebKB and Conference datasets were classified without loss of accuracy, even more, time needed to classify new Web pages reduced sharply as the number of features were decreased.
The increased popularity of the web has caused the inclusion of huge amount of information to the... more 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.
2008 23rd International Symposium on Computer and Information Sciences, 2008
... Selma Ayşe Özel, Esra Saraç Computer Engineering Department, Çukurova University, Faculty of ... more ... Selma Ayşe Özel, Esra Saraç Computer Engineering Department, Çukurova University, Faculty of Engineering and Architecture 01330 Balcalı, Adana ... Ulusoy, G. Özsoyoğlu, and ZM Özsoyoğlu, “Topic-Centric Querying of Web Information Resources”, Lecture Notes in Computer ...
ABSTRACT Increase in the amount of information on the Web has caused the need for accurate automa... more ABSTRACT Increase in the amount of information on the Web has caused the need for accurate automated classifiers for Web pages to maintain Web directories and to increase search engines' performance. As every (HTML/XML) tag and every term on each Web page can be considered as a feature, we need efficient methods to select best features to reduce feature space of the Web page classification problem. In this study, our aim is to apply a recent optimization technique namely the firefly algorithm (FA), to select best features for Web page classification problem. The firefly algorithm (FA) is a metaheuristic algorithm, inspired by the flashing behavior of fireflies. In this study, we use FA to select a subset of features, and to evaluate the fitness of the selected features J48 classifier of the Weka data mining tool is employed. WebKB and Conference datasets were used to evaluate the effectiveness of the proposed feature selection system. We observed that when a subset of features are selected by using FA, WebKB and Conference datasets were classified without loss of accuracy, even more, time needed to classify new Web pages reduced sharply as the number of features were decreased.
The increased popularity of the web has caused the inclusion of huge amount of information to the... more 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.
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