oAdaBoost - An AdaBoost Variant for Ordinal Classification

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Costa, J (Author) Other; Jaime S Cardoso (Author) FEUP

Conference proceedings International

Title: ICPRAM 2015 - Proceedings of the International Conference on Pattern Recognition Applications and Methods, Volume 1, Lisbon, Portugal, 10-12 January, 2015.

Pages: 68-76

4th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2015

10 January 2015 through 12 January 2015

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00G-F75

Abstract (EN): Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order; however, there is not a precise notion of the distance between classes. The Data Replication Method was proposed as tool for solving the ODC problem using a single binary classifier. Due to its characteristics, the Data Replication Method is straightforwardly mapped into methods that optimize the decision function globally. However, the mapping process is not applicable when the methods construct the decision function locally and iteratively, like decision trees and ADABOOST (with decision stumps). In this paper we adapt the Data Replication Method for ADABOOST, by softening the constraints resulting from the data replication process. Experimental comparison with state-of-the-art ADABOOST variants in synthetic and real data show the advantages of our proposal.

Language: English

Type (Professor's evaluation): Scientific