Advertisement

A Model of Machine Learning Based on User Preference of Attributes

  • Yiyu Yao
  • Yan Zhao
  • Jue Wang
  • Suqing Han
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4259)

Abstract

A formal model of machine learning by considering user preference of attributes is proposed in this paper. The model seamlessly combines internal information and external information. This model can be extended to user preference of attribute sets. By using the user preference of attribute sets, user preferred reducts can be constructed.

Keywords

Utility Function Preference Relation Linear Order User Preference External Information 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Blum, A.L., Langley, P.: Selection of relevant features and examples in machine learning. Artificial Intelligence 97, 245–271 (1997)MATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Fishburn, P.C.: Utility Theory for Decision-Making. John Wiley & Sons, New York (1970)MATHGoogle Scholar
  3. 3.
    Han, S.Q., Wang, J.: Reduct and attribute order. Journal of Computer Science and Technology archive 19(4), 429–449 (2004)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Jain, A., Duin, P., Mao, J.: Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4–37 (2000)CrossRefGoogle Scholar
  5. 5.
    Kohavi, R., John, G.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)MATHCrossRefGoogle Scholar
  6. 6.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24(6), 833–849 (2003)MATHCrossRefGoogle Scholar
  7. 7.
    Yao, Y.Y., Chen, Y.H., Yang, X.D.: A measurement-theoretic foundation for rule interestingness evaluation. In: Proceedings of Workshop on Foundations and New Directions in Data Mining in the Third IEEE International Conference on Data Mining (ICDM 2003), pp. 221–227 (2003)Google Scholar
  8. 8.
    Yao, Y.Y., Zhao, Y., Wang, J.: On reduct construction algorithms. In: Proceedings of the First International Conference on Rough Sets and Knowledge Technology, pp. 297–304 (2006)Google Scholar
  9. 9.
    Ziarko, W.: Rough set approaches for discovering rules and attribute dependencies. In: Klösgen, W., Żytkow, J.M. (eds.) Handbook of Data Mining and Knowledge Discovery, Oxford, pp. 328–339 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yiyu Yao
    • 1
  • Yan Zhao
    • 1
  • Jue Wang
    • 2
  • Suqing Han
    • 2
  1. 1.Department of Computer ScienceUniversity of ReginaRegina, SaskatchewanCanada
  2. 2.Laboratory of Complex Systems and Intelligence Science Institute of AutomationChinese Academy of ScienceBeijingChina

Personalised recommendations