Title :
Using Links to Aid Web Classification
Author :
Xie, Wei ; Mammadov, Musa ; Yearwood, John
Author_Institution :
Univ. of Ballarat, Ballarat
Abstract :
In this paper, we will present a new approach of using link information to improve the accuracy and efficiency of web classification. However, different from others, we only use the mappings between linked documents and their own class or classes. In this case, we only need to add a few features called linked-class features into the datasets. We apply SVM and BoosTexter for classification. We show that the classification accuracy can be improved based on mixtures of ordinary word features and out-linked-class features. We analyze and discuss the reason of this improvement.
Keywords :
Internet; classification; support vector machines; text analysis; BoosTexter; SVM; Web classification; Web page; ordinary word feature; out-linked-class feature; Australia; Data mining; Informatics; Spatial databases; Support vector machine classification; Support vector machines; Text categorization; Uniform resource locators; Web pages; Web sites;
Conference_Titel :
Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on
Conference_Location :
Melbourne, Qld.
Print_ISBN :
0-7695-2841-4
DOI :
10.1109/ICIS.2007.191