Feature selection based on partial ordered set of classifiers
作者： Evan R. Kirshenbaum , George H. Forman
DOI:
关键词: Artificial intelligence 、 Computer science 、 Data mining 、 Pattern recognition 、 Random subspace method 、 Classifier (UML) 、 Ordered set 、 Cascading classifiers 、 Feature selection
摘要: A partial order among a set of classifiers is constructed, where the indicates which can be input as features for other classifiers. Based on order, function based an output one classifier selected feature another classifier.
google.com.au 参考文章(22)
Joseph S. Rosen, Multistage machine learning process ,(1999)
Raya Fratkina, Max Copperman, Scott Waterman, Mark Angel, Samir Mahendra, Scott Huffman, Allen Cypher, Denis Lynch, Wendy Fritzke, Shailaja Venkatsubramanyan, Efficient and cost-effective content provider for customer relationship management (CRM) or other applications ,(2002)
Stephane Chiocchetti, George H. Forman, Preparing data for machine learning ,(2004)
Charles J. Northrup, Access-method-independent exchange 3 ,(1994)
Piero Patrone Bonissone, Rajesh Venkat Subbu, Weizhong Yan, Anindya Chakraborty, Richard Paul Messmer, System and process for multivariate adaptive regression splines classification for insurance underwriting suitable for use by an automated system ,(2003)
Robert Walter Schreiber, System and method for enabling multi-indexing of objects ,(2000)
Carl G. DeMarcken, Gregory R. Galperin, Method and apparatus for providing availability of airline seats ,(2000)
Eric J Anderholm, System and method for pattern recognition ,(1998)
Huan Liu, Edward R Dougherty, Jennifer G Dy, Kari Torkkola, Eugene Tuv, Hanchuan Peng, Chris Ding, Fuhui Long, Michael Berens, Lance Parsons, Lei Yu, Zheng Zhao, George Forman, Evolving feature selection IEEE Intelligent Systems. ,vol. 20, pp. 64- 76 ,(2005) , 10.1109/MIS.2005.105