A Web page classification system based on a genetic algorithm using tagged-terms as features

Özel, Selma Ayşe

doi:10.1016/j.eswa.2010.08.126

Cited by 67 publications

(31 citation statements)

References 27 publications

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“…So far, text classification has been successfully applied to various domains such as topic detection (Ghiassi, Olschimke, Moon, & Arnaudo, 2012), spam e-mail filtering (Gunal, Ergin, Gulmezoglu, & Gerek, 2006), SMS spam filtering (Uysal, Gunal, Ergin, & Gunal, 2012), author identification (Cheng, Chandramouli, & Subbalakshmi, 2011), web page classification (Ozel, 2011) and sentiment analysis (Maks & Vossen, 2012).…”

Section: Introductionmentioning

confidence: 99%

The impact of preprocessing on text classification

Uysal

Günal

2014

Information Processing & Management

525

234

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Section: Introductionmentioning

confidence: 99%

The impact of preprocessing on text classification

Uysal

Günal

2014

Information Processing & Management

525

234

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“…Typical text classification framework consists of a feature extraction mechanism that extracts numerical information from raw text documents, and a classifier that carries out the classification process using a prior knowledge of labeled data. Text classification has been successfully deployed to various domains such as topic detection [5], spam e-mail filtering [16,19], author identification [9,11], and web page classification [2,7,33].…”

Section: Introductionmentioning

confidence: 99%

A novel probabilistic feature selection method for text classification

Uysal

Günal

2012

Knowledge-Based Systems

277

128

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“…The experimental results indicated that discriminating power measure is useful for reducing redundancy and noise of features. Özel [20] presented a genetic algorithm based approach for web page classification, which takes HTML tags and tags of terms as features for classification. The best weights of each feature were determined with the use of genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Artificial Immune System Based Web Page Classification

Onan

2015

Software Engineering in Intelligent Systems

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Abstract. Automated classification of web pages is an important research direction in web mining, which aims to construct a classification model that can classify new instances based on labeled web documents. Machine learning algorithms are adapted to textual classification problems, including web document classification. Artificial immune systems are a branch of computational intelligence inspired by biological immune systems which is utilized to solve a variety of computational problems, including classification. This paper examines the effectiveness and suitability of artificial immune system based approaches for web page classification. Hence, two artificial immune system based classification algorithms, namely Immunos-1 and Immunos-99 algorithms are compared to two standard machine learning techniques, namely C4.5 decision tree classifier and Naïve Bayes classification. The algorithms are experimentally evaluated on 50 data sets obtained from DMOZ (Open Directory Project). The experimental results indicate that artificial immune based systems achieve higher predictive performance for web page classification.

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A Web page classification system based on a genetic algorithm using tagged-terms as features

Cited by 67 publications

References 27 publications

The impact of preprocessing on text classification

The impact of preprocessing on text classification

A novel probabilistic feature selection method for text classification

Artificial Immune System Based Web Page Classification

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