Sciweavers

Share
ICMLA
2010

Classification Models with Global Constraints for Ordinal Data

9 years 8 months ago
Classification Models with Global Constraints for Ordinal Data
Ordinal classification is a form of multi-class classification where there is an inherent ordering between the classes, but not a meaningful numeric difference between them. Although conventional methods, designed for nominal classes or regression problems, can be used to solve the ordinal data problem, there are benefits in developing models specific to this kind of data. This paper introduces a new rationale to include the information about the order in the design of a classification model. The method encompasses the inclusion of consistency constraints between adjacent decision regions. A new decision tree and a new nearest neighbour algorithms are then designed under that rationale. An experimental study with artificial and real data sets verifies the usefulness of the proposed approach. Keywords-Classification; ordinal data; decision tree; knearest neighbour
Jaime S. Cardoso, Ricardo Sousa
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICMLA
Authors Jaime S. Cardoso, Ricardo Sousa
Comments (0)
books