C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision extension of rough sets
Fundamenta Informaticae - Special issue: rough sets
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Automatic Web-Page Classification by Using Machine Learning Methods
WI '01 Proceedings of the First Asia-Pacific Conference on Web Intelligence: Research and Development
Consistency-based search in feature selection
Artificial Intelligence
To identify suspicious activity in anomaly detection based on soft computing
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Independent component analysis and rough fuzzy based approach to web usage mining
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
A rough margin based support vector machine
Information Sciences: an International Journal
Mining usage web log via independent component analysis and rough fuzzy
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
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Recently Web-pages on the World Wide Web are explosively increasing, and it is now required for portal sites such as Yahoo! service having directory-style search engines to classify Web-pages into many categories automatically. This paper investigates how rough settheory can help select relevant features for Web-page classification. Our experimental results show that the combination of the rough set-aided feature selection method and the Support Vector Machine with a linear kernel is quite useful in practice to classify Web-pages into many categories because not only the performance gives acceptable accuracy but also the high dimensionality reduction is achieved without depending on arbitrary thresholds for feature selection.