---
title: "On Biases in Estimating Multi-Valued Attributes"
authors:
  - "Igor Kononenko"
doi: ""
url: "https://ijcai.org/proceedings/1995/134"
pdf: "https://ijcai.org/proceedings/1995/0134.pdf"
venue: "Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence"
year: 1995
pages: "1034-1039"
track: "Learning"
keywords:
---

# On Biases in Estimating Multi-Valued Attributes

Igor Kononenko

*Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence*, 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.