Oadaboost an adaboost Variant for Ordinal Classification

Abstract

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.