oAdaBoost - An AdaBoost Variant for Ordinal Classification

João Costa, Jaime S. Cardoso

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.

References

  1. Cardoso, J. S. and Cardoso, M. J. (2007). Towards an intelligent medical system for the aesthetic evaluation of breast cancer conservative treatment. Artificial Intelligence in Medicine, 40:115-126.
  2. Cardoso, J. S. and da Costa, J. F. P. (2007). Learning to classify ordinal data: The data replication method. Journal of Machine Learning Research, 8:1393-1429.
  3. Cardoso, J. S. and Sousa, R. (2010). Classification models with global constraints for ordinal data. In Proceedings of The Ninth International Conference on Machine Learning and Applications (ICMLA), pages 71- 77.
  4. Cardoso, J. S. and Sousa, R. (2011). Measuring the performance of ordinal classification. International Journal of Pattern Recognition and Artificial Intelligence, 25(8):1173-1195.
  5. Cardoso, J. S., Sousa, R., and Domingues, I. (2012). Ordinal data classification using kernel discriminant analysis: A comparison of three approaches. In Proceedings of The Eleventh International Conference on Machine Learning and Applications (ICMLA), pages 473-477.
  6. Eibl, G. and Pfeiffer, K. P. (2002). How to make adaboost. m1 work for weak base classifiers by changing only one line of the code. In Machine Learning: ECML 2002, pages 72-83. Springer.
  7. Frank, E. and Hall, M. (2001). A simple approach to ordinal classification. Springer.
  8. Freund, Y., Iyer, R., Schapire, R. E., and Singer, Y. (2003). An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933- 969.
  9. Freund, Y. and Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the Second European Conference on Computational Learning Theory, EuroCOLT 7895, pages 23-37. Springer-Verlag.
  10. Freund, Y., Schapire, R. E., et al. (1996). Experiments with a new boosting algorithm. In ICML, volume 96, pages 148-156.
  11. Lin, H.-T. and Li, L. (2006). Large-margin thresholded ensembles for ordinal regression: Theory and practice. In Balcázar, J., Long, P., and Stephan, F., editors, Algorithmic Learning Theory, volume 4264 of Lecture Notes in Computer Science, pages 319-333.
  12. Lin, H.-T. and Li, L. (2009). Combining ordinal preferences by boosting. In Proceedings ECML/PKDD 2009 Workshop on Preference Learning, pages 69-83.
  13. Sousa, R. and Cardoso, J. S. (2011). Ensemble of decision trees with global constraints for ordinal classification. In International Conference on Intelligent Systems Design and Applications (ISDA).
  14. Sun, B.-Y., Wang, H.-L., Li, W.-B., Wang, H.-J., Li, J., and Du, Z.-Q. (2014). Constructing and combining orthogonal projection vectors for ordinal regression. Neural Processing Letters, pages 1-17.
Download


Paper Citation


in Harvard Style

Costa J. and Cardoso J. (2015). oAdaBoost - An AdaBoost Variant for Ordinal Classification . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 68-76. DOI: 10.5220/0005191600680076


in Bibtex Style

@conference{icpram15,
author={João Costa and Jaime S. Cardoso},
title={oAdaBoost - An AdaBoost Variant for Ordinal Classification},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={68-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005191600680076},
isbn={978-989-758-076-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - oAdaBoost - An AdaBoost Variant for Ordinal Classification
SN - 978-989-758-076-5
AU - Costa J.
AU - Cardoso J.
PY - 2015
SP - 68
EP - 76
DO - 10.5220/0005191600680076