Log-gamma distribution optimisation via maximum likelihood for ordered probability estimates

3Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Ordinal regression considers classification problems where there exist a natural ordering between the categories. In this learning setting, thresholds models are one of the most used and successful techniques. These models are based on the idea of projecting the patterns to a line, which is thereafter divided into intervals using a set of biases or thresholds. This paper proposes a general likelihood-based optimisation framework to better fit probability distributions for ordered categories. To do so, a specific probability distribution (log-gamma) is used, which generalises three commonly used link functions (log-log, probit and complementary log-log). The experiments show that the methodology is not only useful to provide a probabilistic output of the classifier but also to improve the performance of threshold models when reformulating the prediction rule to take these probabilities into account. © 2014 Springer International Publishing.

Cite

CITATION STYLE

APA

Pérez-Ortiz, M., Gutiérrez, P. A., & Hervás-Martínez, C. (2014). Log-gamma distribution optimisation via maximum likelihood for ordered probability estimates. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8480 LNAI, pp. 454–465). Springer Verlag. https://doi.org/10.1007/978-3-319-07617-1_40

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free