Web Page Classification: A Soft Computing Approach

This paper addresses the issue of an adequate representation of a web page, to perform further on classification and data mining. The approach focuses the textual part of web pages, which are represented by a two-dimension vector. The vector components are sorted by the relevance of each word in the text. Two approaches, analytical and fuzzy, that take advantage of characteristics of the HTML language are presented to compute the word relevance. Both models are contrasted in learning and classification tasks, to evaluate the suitability of each approach. The experiments show an obvious improvement of fuzzy method versus analytical one. The analytical and fuzzy approaches here presented are general, in the sense that every characteristic of the web pages could be easily integrated without additional cost.

The Web is the largest collection of electronically accessible documents which make the richest source of information in the world. The problem with the Web is that this information is not well structured and organized so that it would be easily retrieved. Web page classification used for managing and extract relevant information from Web content and in order to effectively use the knowledge available on the Web. In this dissertation a rule base system used to contract the system classifier for solve Web online classification by assigning each scanned HTML document to their class. The aim of this thesis is to design and implement HTML document classification system that is able to classify the HTML documents according to their class (category). The proposed system is designed to solve and improve web page classification problem using Rule-Based Classifier that check the HTML content of each entered URL address for system rule occurrences. The Proposed system enhances other web page classification system by making the system work online.

2013

The web is a huge repository of information and there is a need for categorizing web documents to facilitate the search and retrieval of documents. Web document classification plays an important role in information organization and retrieval.This paper presents a fuzzy set based approach for automatically classifying web documents into one of the classes represented by a set of training documents belonging to a number of classes. Using same word to represent more than one meaning and many words representing one meaning lead to ambiguity especially in web environment where numbers of users are very large. This problem is tackled using fuzzy association wherein each pair of words has a value associated with it. This helps in distinguishing it with other such pairs of words and thus helps in tackling ambiguities. The approach present in this paper does not require any parameter to be given by the user and hence is independent of any bias that may occur due to user input. It requires a ...

Web Page Classification is one of the common problems of the today's Internet. In this paper, an automatic Web page classification system is introduced. The proposed system tries to increase the accuracy of a web page classification via combine the well-known Naïve Bayesian algorithm, Support Vector Machine and K-Nearest Neighbor. The experimental results shows that the performance of classifying web page by hybrid Naïve Bayesian classifier, Support Vector Machine and K-Nearest Neighbor algorithm is better than using Naïve Bayesian alone as always used to get the highest and fastest classifier or using K-Nearest Neighbor alone or using Support Vector Machine alone to reduce the false positive rate and get highest accuracy. The experimental results, applied on 10.000 web pages (30% for training process and 70% for testing process), showed a high efficiency with the less number of false positive rate (on average) 0%, the true positive rate (on average) 1%, F-measure (on average) 1% and overall accuracy rate (on average) 99.98%.

International Journal of Data Mining, Modelling and Management, 2013

The boom in the use of Web and its exponential growth are now well known. The amount of textual data available on the Web is estimated to be in the order of one terra byte, in addition to images, audio and video. This has imposed additional challenges to the Web directories which help the user to search the Web by classifying selected Web documents into subject. Manual classification of web pages by human expertise also suffers from the exponential increase in the amount of Web documents. Instead of using the entire web page for classifying it, this article emphasizes the need for automatic web page classification using minimum number of features in it. A method for generating such optimum number of features for web pages is also proposed. Machine learning classifiers are modeled using these optimum features. Experiments on the bench marking data sets with these machine learning classifiers have shown promising improvement in classification accuracy.

V International Enformatika Conference(IEC …, 2005

Abstract— In this study, a fuzzy similarity approach for Arabic web pages classification is presented. The approach uses a fuzzy term-category relation by manipulating membership degree for the training data and the degree value for a test web page. Six measures are used and ...

International Computer Software and Applications Conference, 2002

In this paper, a method of automatically classifying Web documents into a set of categories using the fuzzy association concept is proposed. Using the same word or vocabulary to describe different entities creates ambiguity, especially in the Web environment where the user population is large. To solve this problem, fuzzy association is used to capture the relationships among different index

IOSR Journal of Computer Engineering, 2012

The World Wide Web is growing at an uncontrollable rate. Hundreds of thousands of web sites appear every day, with the added challenge of keeping the web directories up-to-date. Further, the uncontrolled nature of web presents difficulties for Web page classification. As the number of Internet users is growing, so is the need for classification of web pages with greater precision in order to present the users with web pages of their desired class. However, web page classification has been accomplished mostly by using textual categorization methods. Herein, we propose a novel approach for web page classification that uses the HTML information present in a web page for its classification. There are many ways of achieving classification of web pages into various domains. This paper proposes an entirely new dimension towards web page classification using Artificial Neural Networks (ANN).

Expert Systems with Applications, 2009

To help the growing qualitative and quantitative demands for information from the WWW, efficient automatic Web page classifiers are urgently needed. However, a classifier applied to the WWW faces a huge-scale dimensionality problem since it must handle millions of Web pages, tens of thousands of features, and hundreds of categories. When it comes to practical implementation, reducing the dimensionality is a critically important challenge. In this paper, we propose a fuzzy ranking analysis paradigm together with a novel relevance measure, discriminating power measure (DPM), to effectively reduce the input dimensionality from tens of thousands to a few hundred with zero rejection rate and small decrease in accuracy. The two-level promotion method based on fuzzy ranking analysis is proposed to improve the behavior of each relevance measure and combine those measures to produce a better evaluation of features. Additionally, the DPM measure has low computation cost and emphasizes on both positive and negative discriminating features. Also, it emphasizes classification in parallel order, rather than classification in serial order. In our experimental results, the fuzzy ranking analysis is useful for validating the uncertain behavior of each relevance measure. Moreover, the DPM reduces input dimensionality from 10,427 to 200 with zero rejection rate and with less than 5% decline (from 84.5% to 80.4%) in the test accuracy. Furthermore, to consider the impacts on classification accuracy for the proposed DPM, the experimental results of China Time and Reuter-21578 datasets have demonstrated that the DPM provides major benefit to promote document classification accuracy rate. The results also show that the DPM indeed can reduce both redundancy and noise features to set up a better classifier.

International Journal of Computer Applications, 2018

Classification of Web pages is one of the challenging and important task as there is an increase in web pages in day to day life provided by internet. There are many ways of classifying web pages based on different approach and features. This paper explains some of the approaches and algorithms used for the classification of webpages. Web pages are allocated to predetermined categories which is done mainly according to their content in Web page classification. The important technique for web mining is web page classification because classifying the web pages of interesting class is the initial step of data mining. The agenda of this paper is first to introduce the concepts related to web mining and then to provide a comprehensive review of different classification techniques.