Minimum Splits Based Discretization for Continuous Features
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence. Learning. Pages 942-950.
Abstract
Discretization refers to splitting the range of continuous values into intervals so as to provide useful information about classes. This is usually done by minimizing a goodness measure, subject to constraints such as the maximal number of intervals, the minimal number of examples per interval, or some stopping criterion for splitting. We take a different approach by searching for minimum splits that minimize the number of intervals with respect to a threshold of impurity (i.e., badness). We propose a "total entropy" motivated selection of the "best" split from minimum splits, without requiring additional constraints. Experiments show that the proposed method produces better decision trees.
@inproceedings{ijcai1997p136,
title = {Minimum Splits Based Discretization for Continuous Features},
author = {Wang, Ke and Goh, Han Chong},
booktitle = {Proceedings of the Fifteenth International Joint Conference on
Artificial Intelligence, {IJCAI-97}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Martha E. Pollack},
pages = {942--950},
year = {1997},
note = {Learning},
url = {https://ijcai.org/proceedings/1997/136},
}
TY - CPAPER TI - Minimum Splits Based Discretization for Continuous Features AU - Wang, Ke AU - Goh, Han Chong T2 - Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, IJCAI-97 PB - International Joint Conferences on Artificial Intelligence PY - 1997 SP - 942 EP - 950 UR - https://ijcai.org/proceedings/1997/136 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
