Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/66626

Title

Adding monotonicity to learning algorithms may impair their accuracy

Author(s)

Ben-David, Arie;  Sterling, Leon;  Tran, TriDat

Abstract

Ordinal (i.e., ordered) classifiers are used to make judgments that we make on a regular basis, both at work and at home. Perhaps surprisingly, there have been no comprehensive studies in the scientific literature comparing the various ordinal classifiers. This paper compares the accuracy of five ordinal and three non-ordinal classifiers on a benchmark of fifteen real-world datasets. The results show that the ordinal classifiers that were tested had no meaningful statistical advantage over the corresponding non-ordinal classifiers. Furthermore, the ordinal classifiers that guaranteed monotonic classifications showed no meaningful statistical advantage over a majority-based classifier. We suggest that the tested ordinal classifiers did not properly utilize the order information in the presence of non-monotonic noise.

Publication type

Journal article

Source

Expert Systems with Applications, Vol. 36, no. 3, part 2 (Apr 2009), pp. 6627-6634

Publication year

2009

Keyword(s)

Classifiers;  Comprehensive studies;  Data mining;  Data sets;  Information management;  Learning algorithms;  Learning systems;  Machine learning;  Monotonic classification;  Monotonicity;  Order informations;  Ordinal classification;  Robot learning;  Scientific literatures

Publisher

Pergamon

ISSN

0957-4174

Publisher URL

http://dx.doi.org/10.1016/j.eswa.2008.08.021

Copyright

Copyright © 2009.

Peer reviewed