On Biases in Estimating Multi-Valued Attributes
Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. Learning. Pages 1034-1039.
Abstract
We ana lyse the biases of eleven measures for estimating the quality of the multi-valued attributes. The values of information gain, ]measure, gini-index, and relevance tend to linearly increase with the number of values of an attribute. The values of gain-ratio, distance measure, Relief , and the weight of evidence decrease for informative attributes and increase for irrelevant attributes. The bias of the statistic tests based on the chi-square distribution is similar but these functions are not able to discriminate among the attributes of different quality. We also introduce a new function based on the MDL principle whose value slightly decreases with the increasing number of attribute's values.
@inproceedings{ijcai1995p134,
title = {On Biases in Estimating Multi-Valued Attributes},
author = {Kononenko, Igor},
booktitle = {Proceedings of the Fourteenth International Joint Conference on
Artificial Intelligence, {IJCAI-95}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Chris S. Mellish},
pages = {1034--1039},
year = {1995},
note = {Learning},
url = {https://ijcai.org/proceedings/1995/134},
}
TY - CPAPER TI - On Biases in Estimating Multi-Valued Attributes AU - Kononenko, Igor T2 - Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, IJCAI-95 PB - International Joint Conferences on Artificial Intelligence PY - 1995 SP - 1034 EP - 1039 UR - https://ijcai.org/proceedings/1995/134 ER -Copied
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