Ordinal regression with continuous pairwise preferences

Autor:	Hui Wang, Maria Dobrska, William Blackburn
Rok vydání:	2011
Předmět:	Ordinal data Preference learning computer.software_genre Ordinal regression Ordinal optimization Artificial Intelligence Metric (mathematics) Collaborative filtering Learning to rank Pairwise comparison Computer Vision and Pattern Recognition Data mining computer Software Mathematics
Zdroj:	International Journal of Machine Learning and Cybernetics. 3:59-70
ISSN:	1868-808X 1868-8071
Popis:	We investigate the problem of data-driven ordinal regression—the problem of learning to rank order new data items based on information inherent in existing data items. Ordinal regression shares common features with multi-category classification and metric regression. However, it requires new, tailor-made methodologies to reduce prediction error. The approach has application in various domains, including information retrieval, collaborative filtering and social sciences. We propose a new distribution-independent methodology for ordinal regression based on pairwise preferences employing information about strength of dependency between two data instances, which we refer to as continuous preferences. Our hypothesis is that additional information about strength of preference as well as its direction can improve algorithmic performance. We also provide a novel technique for deriving ordinal regression labels from pairwise information. Experimental results on real-world ordinal and metric regression data sets confirm usefulness of the methodology compared with other state-of-the-art approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::54506a31c7fd3ff374b798ff647b3477 https://doi.org/10.1007/s13042-011-0036-x Zobrazit plný text záznamu Full text from SpringerLink