Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Image Analysis for Efficient Categorization of Image-based Spam E-mail
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Neighborhood rough set based heterogeneous feature subset selection
Information Sciences: an International Journal
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
Image coding using wavelet transform
IEEE Transactions on Image Processing
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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.