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Investigation on AQ11, ID3 and the principle of discernibility matrix

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Abstract

The principle of discernibility matrix serves as a tool to discuss and analyze two algerithms of traditional inductive machine learning, AQ11 and ID3. The results are: (1) AQ11 and its family can be completely specified by the principle of discernibility matrix; (2) ID3 can be partly, but not naturally, specified by the principle of discernibility matrix; and (3) The principle of discernibility matrix is employed to analyze Cendrowska sample set, and it shows the weaknesses of knowledge representation style of decision tree in theory.

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Authors and Affiliations

  1. Institute of Automation, The Chinese Academy of Sciences, 100080, Beijing, P.R. China

    Wang Jue, Cui Jia & Zhao Kai

Authors
  1. Wang Jue
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  2. Cui Jia
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  3. Zhao Kai
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Corresponding author

Correspondence to Wang Jue.

Additional information

This research is partly supported by the National ‘863’ High-Tech Programme (No. 863-306-ZT06-07-1) and NKPSF (G1998030508).

WANG Jue is a professor at Institute of Automation, the Chinese Academy of Sciences, and IEEE Senior Member. His research interests are knowledge representation, ANN, GA, multi-agent system, machine learning and data mining.

CUI Jia received her B.S. degree from University of Science and Technology of China in 1997. She is currently an M.S. candidate at Institute of Automation, the Chinese Academy of Sciences. Her research interests are rough sets, association rules.

ZHAO Kai received his B.S. degree from Beijing Institute of Technology in 1993, and Ph.D. degree from Institute of Automation, the Chinese Academy of Sciences in 1999. His research interests are adaptation systems, genetic programming and data mining.

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Wang, J., Cui, J. & Zhao, K. Investigation on AQ11, ID3 and the principle of discernibility matrix. J. Comput. Sci. & Technol. 16, 1–12 (2001). https://doi.org/10.1007/BF02948848

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  • DOI: https://doi.org/10.1007/BF02948848

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