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Evaluating Learning Algorithms with Meta-learning Schemes for a Rule Evaluation Support Method Based on Objective Indices

  • Hidenao Abe
  • Shusaku Tsumoto
  • Miho Ohsaki
  • Hideto Yokoi
  • Takahira Yamaguchi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4303)

Abstract

In this paper, we present evaluations of learning algorithms for a novel rule evaluation support method in data mining post-processing, which is one of the key processes in a data mining process. It is difficult for human experts to evaluate many thousands of rules from a large dataset with noises completely. To reduce the costs of rule evaluation task, we have developed the rule evaluation support method with rule evaluation models, which are learned from a dataset consisted of objective indices and evaluations of a human expert for each rule. To enhance adaptability of rule evaluation models, we introduced a constructive meta-learning system to choose proper learning algorithms for constructing them. Then, we have done a case study on the meningitis data mining result, the hepatitis data mining results and rule sets from the eight UCI datasets.

Keywords

Training Dataset Human Expert Weight Vote Rule Evaluation Objective Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hidenao Abe
    • 1
  • Shusaku Tsumoto
    • 1
  • Miho Ohsaki
    • 2
  • Hideto Yokoi
    • 3
  • Takahira Yamaguchi
    • 4
  1. 1.Department of Medical InformaticsShimane University, School of MedicineShimaneJapan
  2. 2.Faculty of EngineeringDoshisha University 
  3. 3.Department of Medical InformaticsKagawa University Hospital 
  4. 4.Faculty of Science and TechnologyKeio University 

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