A novel approach for effective web page classification

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.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.