The Community for Technology Leaders
Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on (2012)
Macau, China China
Dec. 4, 2012 to Dec. 7, 2012
ISBN: 978-1-4673-6057-9
pp: 175-179
ABSTRACT
The paper addresses two problems of web content mining, such as scene-region classification (applicable to image annotation), and image based spam detection. To solve these problems, we describe two granular computing (i.e., with rough-fuzzy and rough-wavelet granular spaces) based pattern classification models. These models can be used to design intelligent agents which may provide an improved solution to web mining. Neighborhood rough sets are used in the selection of a subset of these granulated features of models. Both the models explore mutually the advantages of fuzzy/wavelet granulation and neighborhood rough sets. The superiority of these models to other similar methods is established with various performance measures.
INDEX TERMS
computational intelligent agents, Information granulation, pattern classification, web intelligence
CITATION

S. K. Meher, S. K. Pal and S. Dutta, "Granular Computing Models in the Classification of Web Content Data," Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on(WI-IAT), Macau, China China, 2012, pp. 175-179.
doi:10.1109/WI-IAT.2012.138
96 ms
(Ver 3.3 (11022016))