Abstract
This paper proposed an improved decision tree algorithm, ID3+. Through the performance of autonomous backtracking, information gain reduction and surrogate value, our method overcomes some ID3’s disadvantages, such as preference bias and the inability to deal with unknown attribute values. The experimental results show that our method can competitively and efficiently solve the two problems. The first problems often leads to inferior decision trees, while the second limits ID3’s applicability in real-world domains. And the method could be a good start for a more robust decision tree learning system.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xu, M., Wang, JL., Chen, T. (2006). Improved Decision Tree Algorithm: ID3+ . In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing in Signal Processing and Pattern Recognition. Lecture Notes in Control and Information Sciences, vol 345. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37258-5_15
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DOI: https://doi.org/10.1007/978-3-540-37258-5_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37257-8
Online ISBN: 978-3-540-37258-5
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