A simple, structure-sensitive approach for Web document classification

Alex Markov, Mark Last

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In this paper we describe a new approach to classification of web documents. Most web classification methods are based on the vector space document representation of information retrieval. Recently the graph based web document representation model was shown to outperform the traditional vector representation using k-Nearest Neighbor (k-NN) classification algorithm. Here we suggest a new hybrid approach to web document classification built upon both, graph and vector representations. K-NN algorithm and three benchmark document collections were used to compare this method to graph and vector based methods separately. Results demonstrate that we succeed in most cases to outperform graph and vector approaches in terms of classification accuracy along with a significant reduction in classification time.

Original languageEnglish
Title of host publicationAdvances in Web Intelligence - Third International Atlantic Web Intelligence Conference, AWIC 2005, Proceedings
PublisherSpringer Verlag
Pages293-298
Number of pages6
ISBN (Print)3540262199, 9783540262190
DOIs
StatePublished - 1 Jan 2005
EventThird International Atlantic Web Intelligence Conference on Advances in Web Intelligence, AWIC 2005 - Lodz, Poland
Duration: 6 Jun 20059 Jun 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3528 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceThird International Atlantic Web Intelligence Conference on Advances in Web Intelligence, AWIC 2005
Country/TerritoryPoland
CityLodz
Period6/06/059/06/05

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