Abstract
An adaptable structure to build a classification tree is presented. From such structure different existing classification trees can be obtained, but also we can build new ones, and compare the results of different trees (classification error, tree size, number of levels or other defined criteria). We use the adaptable scheme to emulate ID3, C4.5 and M5 trees, but also create a new tree (called general tree), and results obtained shows that we can obtain the same results with the original trees, and for the case of the general tree, its results are very close to the better classifier tree of the three studied.
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Keywords
- Root Mean Square Error
- Regression Tree
- Regression Problem
- Continuous Attribute
- Duchenne Muscular Dystrophy
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© 2014 Springer International Publishing Switzerland
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Unda-Trillas, E., Rivera-Rovelo, J. (2014). A Method to Build Classification and Regression Trees. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_55
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DOI: https://doi.org/10.1007/978-3-319-12568-8_55
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12567-1
Online ISBN: 978-3-319-12568-8
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