Rank Aggregation Based Text Feature Selection

Filtering feature selection method (filtering method, for short) is a well-known feature selection strategy in pattern recognition and data mining. Filtering method outperforms other feature selection methods in many cases when the dimension of features is large. There are so many filtering methods...

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Bibliographic Details
Published in:	Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 Vol. 1; pp. 165 - 172
Main Authors:	Wu, Ou, Zuo, Haiqiang, Zhu, Mingliang, Hu, Weiming, Gao, Jun, Wang, Hanzi
Format:	Conference Proceeding
Language:	English
Published:	Washington, DC, USA IEEE Computer Society 15.09.2009 IEEE
Series:	ACM Conferences
Subjects:	Applied computing > Document management and text processing Automation Computer science Computing methodologies > Machine learning > Machine learning algorithms > Feature selection Computing methodologies > Modeling and simulation > Model development and analysis Computing methodologies > Modeling and simulation > Model development and analysis > Model verification and validation Computing methodologies > Modeling and simulation > Model development and analysis > Modeling methodologies Conferences Content addressable storage Data mining Fuses Information filtering Information filters Information systems > Information retrieval > Retrieval models and ranking Information systems > Information retrieval > Retrieval tasks and goals Intelligent agent Markov chains Mathematics of computing > Probability and statistics > Probabilistic representations > Markov networks Mathematics of computing > Probability and statistics > Stochastic processes > Markov processes Pattern recognition Rank aggregation Text feature selection Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes Rank aggregation Text feature selection Markov chains
ISBN:	0769538010, 9780769538013
Online Access:	Get full text
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