An Ant Colony Optimization Based Feature Selection for Web Page Classification

Abstract

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.

Swarm Intelligence Algorithms for Feature Selection: A Review

“…For the same reason as in Table 2, we excluded the Group Hunting section. [49] FA [56] ABC [33,41] 13 GSO [50] FA [53] FA [58,81] ACO [82] ACO [83] Based on the survey, we can state that Bio-Medical Engineering is the most used field for algorithms' testing or applying. Researchers in [58] proposed the Chaos FA algorithm for heart disease prediction; the Bacterial Memetic Algorithm based feature selection for surface EMG-based hand motion recognition in long-term use was suggested by [85].…”

“…The researchers in [33] presented a novel method named IFAB based on an ABC algorithm for feature selection, and the authors in [53] used the FA algorithm for blind image steganalysis. Some other problems tackled in this field comprise fault diagnosis of complex structures with the BFO algorithm [64], skin detection based background removal for enhanced face recognition with adaptive BPSO [89], identifying malicious web domains with BPSO in [88], and web page classification with ACO [83].…”

A binary ABC algorithm based on advanced similarity scheme for feature selection

“…In this model, grid based ACO was implemented to optimize the parameters of SVM, and feature subset selection was performed through F-statistics. Nemati et al [14] introduced a parallel combined version of ACO and GA for feature selection in protein function prediction.Sarac and Ozel [53] proposed an ACO based feature selection approach for web page classification, in which feature extraction is applied before feature selection to group similar HTML tags together, i.e., to reduce the feature space. More information on ACO based feature selection can be found in [50,54].…”

Effects of Feature Extraction and Classification Methods on Cyberbully Detection

“…Then, features that have a document frequency which is less than 0.1% of the number of documents in the training set are eliminated to remove misspelled words or words which are used very rarely. According to Salton [44] and our previous studies [45][46][47] using document frequency value of terms allows us to eliminate misspelled or unimportant terms from the feature space. The numbers of features obtained according to the three feature extraction method with a 0.1% document frequency filtering are given in Table 4.…”