{"type":"ScholarlyArticle","title":"On Biases in Estimating Multi-Valued Attributes","authors":[{"name":"Igor Kononenko","slug":"igor-kononenko","given":"Igor","family":"Kononenko","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/1995/134","pdf":"https://ijcai.org/proceedings/1995/0134.pdf","markdown":"https://ijcai.org/proceedings/1995/134.md","bibtex":"https://ijcai.org/proceedings/1995/bibtex/134","volume":{"slug":"1995","title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1995","url":"https://ijcai.org/proceedings/1995/","year":1995,"location":"Montréal, Québec, Canada","dates":"","editors":"Chris S. Mellish","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Learning","track":"Learning","number":134,"pages":{"first":1034,"last":1039},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}