Using Heuristics to Speed up Induction on Continuous-Valued Attributes
Abstract
Induction of decision trees in domains with continuous-valued attributes is computationally expensive due to the evaluation of every possible test on these attributes. As the number of tests to be considered grows linearly with the number of examples, this poses a problem for induction on large databases. Two variants of a heuristic, based on the possible difference of the entropy-minimization selection-criterion between two tests, are proposed and compared to a previously known heuristic. Empirical results with real-world data confirm that the heuristics can reduce the computational effort significantly without any change in the induced decision trees.
Cite
Text
Seidelmann. "Using Heuristics to Speed up Induction on Continuous-Valued Attributes." European Conference on Machine Learning, 1993. doi:10.1007/3-540-56602-3_156Markdown
[Seidelmann. "Using Heuristics to Speed up Induction on Continuous-Valued Attributes." European Conference on Machine Learning, 1993.](https://mlanthology.org/ecmlpkdd/1993/seidelmann1993ecml-using/) doi:10.1007/3-540-56602-3_156BibTeX
@inproceedings{seidelmann1993ecml-using,
title = {{Using Heuristics to Speed up Induction on Continuous-Valued Attributes}},
author = {Seidelmann, Günter},
booktitle = {European Conference on Machine Learning},
year = {1993},
pages = {390-395},
doi = {10.1007/3-540-56602-3_156},
url = {https://mlanthology.org/ecmlpkdd/1993/seidelmann1993ecml-using/}
}