Description

Data was used to test 2 tier approach with learning from positive and negative examples

Related Papers

  • Alexander K. Seewald. Dissertation Towards Understanding Stacking Studies of a General Ensemble Learning Scheme ausgefuhrt zum Zwecke der Erlangung des akademischen Grades eines Doktors der technischen Naturwissenschaften. [link]
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  • Huan Liu and Rudy Setiono. To appear in Proceedings of IEA-AIE96 FEATURE SELECTION AND CLASSIFICATION -- A PROBABILISTIC WRAPPER APPROACH. Department of Information Systems and Computer Science National University of Singapore. [link]
  • Gary M. Weiss and Haym Hirsh. A Quantitative Study of Small Disjuncts: Experiments and Results. Department of Computer Science Rutgers University. 2000. [link]
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  • John G. Cleary and Leonard E. Trigg. Experiences with OB1, An Optimal Bayes Decision Tree Learner. Department of Computer Science University of Waikato. [link]
  • Richard Maclin. Boosting Classifiers Regionally. AAAI/IAAI. 1998. [link]
  • YongSeog Kim and W. Nick Street and Filippo Menczer. Optimal Ensemble Construction via Meta-Evolutionary Ensembles. Business Information Systems, Utah State University. [link]
  • George H. John and Ron Kohavi and Karl Pfleger. Irrelevant Features and the Subset Selection Problem. ICML. 1994. [link]
  • Rudy Setiono. Feedforward Neural Network Construction Using Cross Validation. Neural Computation, 13. 2001. [link]
  • Ida G. Sprinkhuizen-Kuyper and Elena Smirnova and I. Nalbantis. Reliability yields Information Gain. IKAT, Universiteit Maastricht. [link]
  • Oya Ekin and Peter L. Hammer and Alexander Kogan and Pawel Winter. Distance-Based Classification Methods. e p o r t RUTCOR ffl Rutgers Center for Operations Research ffl Rutgers University. 1996. [link]
  • Ron Kohavi and George H. John. Automatic Parameter Selection by Minimizing Estimated Error. Computer Science Dept. Stanford University. [link]
  • Huan Liu and Rudy Setiono. A Probabilistic Approach to Feature Selection - A Filter Solution. ICML. 1996. [link]
  • Alexander K. Seewald. Meta-Learning for Stacked Classification. Austrian Research Institute for Artificial Intelligence. [link]
  • Lorne Mason and Jonathan Baxter and Peter L. Bartlett and Marcus Frean. Boosting Algorithms as Gradient Descent. NIPS. 1999. [link]
  • Chris Drummond and Robert C. Holte. C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling. Institute for Information Technology, National Research Council Canada. [link]
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