Abstract
Quinlan's ID3 machine learning algorithm induces classification trees (rules) from a set of training examples. The algorithm is extremely effective when training examples are composed of attributes whose values are taken from small discrete domains. The classification accuracy of ID3-produced trees on domains whose attributes are many-valued tends to be marginal due to the large number of possible values which may be associated with each attribute. Attempts to solve this problem by a priori grouping of attribute values into distinct subsets has met with limited success. The dynamic ID3 algorithm improves the performance of ID3 on this type of problem by grouping many-valued attributes dynamically as the tree is built. Experimental results are provided which compare the performance of dynamic ID3 with standard ID3 and ID3 in which a priori grouping has been used.
Recommended Citation
R. Gallion et al., "Dynamic ID3: A Symbolic Learning Algorithm For Many-valued Attribute Domains," Proceedings of the ACM Symposium on Applied Computing, pp. 14 - 20, Association for Computing Machinery (ACM), Mar 1993.
The definitive version is available at https://doi.org/10.1145/162754.162766
Department(s)
Computer Science
Second Department
Mathematics and Statistics
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2023 Association for Computeing Machinery (ACM), All rights reserved.
Publication Date
01 Mar 1993
