Authors: J. Alamelu Mangai; V. Santhosh Kumar; S. Appavu Balamurugan
Addresses: Department of Computer Science and Engineering, BITS, Pilani – Dubai Campus, P.O. Box 345055, Dubai, UAE ' Department of Computer Science and Engineering, BITS, Pilani – Dubai Campus, P.O. Box 345055, Dubai, UAE ' Department of Information Technology, Thiagarajar College of Engineering, 625015, TN, Madurai, India
Abstract: With the exponential increase in volume of the WWW every day, web page classification has become tedious. Since with no quality data there is no quality mining results, it is worth to emphasise on fine tuning the data for classification, rather than improving the classifiers themselves. This paper investigates the methods for improving web page classification by feature extraction, selection and data tuning. This paper also proposes a new classification model for web page classification called a probabilistic web page classifier (PWPC). It is based on a probabilistic framework and attribute-value similarity measure (AVS). The proposed method is tested on a benchmarking dataset, WebKB and the performance of PWPC on the fine tuned web pages has exhibited significant accuracy over the traditional machine learning classifiers.
Keywords: feature selection; data tuning; web page classification; machine learning; WebKB; feature extraction; classifiers.
International Journal of Data Mining, Modelling and Management, 2013 Vol.5 No.3, pp.233 - 245
Published online: 09 Aug 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article