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Directly Identify Unexpected Instances in the Test Set by Entropy Maximization

  • Chaofeng Sha
  • Zhen Xu
  • Xiaoling Wang
  • Aoying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

Abstract

In real applications, a few unexpected examples unavoidably exist in the process of classification, not belonging to any known class. How to classify these unexpected ones is attracting more and more attention. However, traditional classification techniques can’t classify correctly unexpected instances, because the trained classifier has no knowledge about these. In this paper, we propose a novel entropy-based method to the problem. Finally, the experiments show that the proposed method outperforms previous work in the literature.

Keywords

Text Data Severe Acute Respiratory Syndrome Nominal Data Positive Class Negative Instance 
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 2009

Authors and Affiliations

  • Chaofeng Sha
    • 1
  • Zhen Xu
    • 1
  • Xiaoling Wang
    • 2
  • Aoying Zhou
    • 2
  1. 1.Department of Computer Science and EngineeringFudan UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Trustworthy Computing Institute of Massive ComputingEast China Normal UniversityShanghaiChina

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