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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Veröffentlicht in:Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 Jg. 1; S. 165 - 172
Hauptverfasser: Wu, Ou, Zuo, Haiqiang, Zhu, Mingliang, Hu, Weiming, Gao, Jun, Wang, Hanzi
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: Washington, DC, USA IEEE Computer Society 15.09.2009
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
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-Zugang:Volltext
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Zusammenfassung: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 proposed in previous work leading to the “selection trouble” that how to select an appropriate filtering method for a given text data set. Since to find the best filtering method is usually intractable in real application, this paper takes an alternative path. We propose a feature selection framework that fuses the results obtained by different filtering methods. In fact, deriving a better rank list from different rank lists, known as rank aggregation, is a hot topic studied in many disciplines. Based on the proposed framework and Markov chains rank aggregation techniques, in this paper, we present two new feature selection methods: FR-MC1 and FR-MC4. We also introduce a perturbation algorithm to alleviate the drawbacks of Markov chains rank aggregation techniques. Empirical evaluation on two public text data sets shows that the two new feature selection methods achieve better or comparable results than classical filtering methods, which also demonstrate the effectiveness of our framework.
ISBN:0769538010
9780769538013
DOI:10.1109/WI-IAT.2009.32