Title
Classifying Web Pages by Genre: An n-Gram Approach
Abstract
The research reported in this paper is part of a larger project on the classification of Web pages by genre. Such classification is a potentially powerful tool in filtering the results of online searches. In this paper, we describe two sets of experiments investigating the automatic classification of Web pages by their genres. In these experiments, our approach is to represent the Web pages by profiles that are composed of fixed-length byte n-grams. The first set of experiments in this study examines the effect of three feature selection measures on the accuracy of Web page classification. The second set of experiments in this study compares the classification accuracy of three classification methods, each using n-gram representations of the Web pages. The classification methods which are compared are a distance function approach, the k-nearest neighbors method, and the support vector machine approach. We also examine a range of n-gram lengths and a range of Web page profile sizes to determine what combination(s) of n-gram length and profile size give the best classification accuracy. Each set of experiments is run on two well-known data sets, 7-Genre and KI-04, for which published results are available.
Year
DOI
Venue
2009
10.1109/WI-IAT.2009.79
Web Intelligence
Keywords
Field
DocType
web page classification,classification method,classification accuracy,n-gram approach,distance function approach,web page profile size,n-gram length,n-gram representation,best classification accuracy,classifying web pages,web page,automatic classification,intelligent agent,support vector machines,web pages,distance function,k nearest neighbor,support vector machine,testing,feature selection,html
Library classification,Byte,Data mining,Web page,Feature selection,Information retrieval,Computer science,Support vector machine,Web query classification,Metric (mathematics),n-gram
Conference
Citations 
PageRank 
References 
3
0.45
15
Authors
3
Name
Order
Citations
PageRank
Jane E. Mason181.56
Michael A. Shepherd249367.67
Jack Duffy31017.57