Classification Models with Global Constraints for Ordinal Data

• Type: Article in International Conference Proceedings Book
• Date: 2010

Authors

Cardoso, JS (Author) FEUP; Sousa, R (Author) Other

Conference proceedings International

Title: Proceedings - 9th International Conference on Machine Learning and Applications, ICMLA 2010

Pages: 71-77

9th International Conference on Machine Learning and Applications, ICMLA 2010

Washington, DC, 12 December 2010 through 14 December 2010

Indexing

ISI Web of Knowledge

Scopus - 6 Citations

Other information

Authenticus ID: P-007-XN9

DOI: 10.1109/icmla.2010.18

Abstract (EN): 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. © 2010 IEEE.

Language: English

Type (Professor's evaluation): Scientific