Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
作者： Lloyd A. Smith , Mark A. Hall
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摘要: … The wrapper approach is generally considered to produce better feature subsets but runs much more slowly than a filter. This paper describes a new filter approach to feature selection …
acm.org 参考文章(13)
Thomas G. Dietterich, Hussein Almuallim, Efficient Algorithms for Identifying Relevant Features Oregon State University. ,(1992)
Lloyd A. Smith, Mark A. Hall, Practical feature subset selection for machine learning Springer. pp. 181- 191 ,(1998)
M. Hall, Correlation-based Feature Selection for Machine Learning PhD Thesis, Waikato Univer-sity. ,(1998)
Craig G. Nevill-Manning, Geoffrey Holmes, Feature selection via the discovery of simple classification rules University of Waikato, Department of Computer Science. ,(1995)
Edwin Ernest Ghiselli, Theory of psychological measurement ,(1964)
Ron Kohavi, Wrappers for Performance Enhancements and Oblivious Decision Graphs. Stanford University. ,(1995)
Kenji Kira, Larry A. Rendell, A Practical Approach to Feature Selection international conference on machine learning. pp. 249- 256 ,(1992) , 10.1016/B978-1-55860-247-2.50037-1
Keki B. Irani, Usama M. Fayyad, Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning international joint conference on artificial intelligence. ,vol. 2, pp. 1022- 1027 ,(1993)
George H John, Ron Kohavi, Karl Pfleger, None, Irrelevant Features and the Subset Selection Problem Machine Learning Proceedings 1994. pp. 121- 129 ,(1994) , 10.1016/B978-1-55860-335-6.50023-4
Mehran Sahami, Daphne Koller, Toward optimal feature selection international conference on machine learning. pp. 284- 292 ,(1996)