SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION

Mariam Kalakech, Philippe Biela, Denis Hamad, Ludovic Macaire

Abstract

Recent feature constraint scores, that analyse must-link and cannot-link constraints between learning samples, reach good performances for semi-supervised feature selection. The performance evaluation is generally based on classification accuracy and is performed in a supervised learning context. In this paper, we propose a semi-supervised performance evaluation procedure, so that both feature selection and classification take into account the constraints given by the user. Extensive experiments on benchmark datasets are carried out in the last section. They demonstrate the effectiveness of feature selection based on constraint analysis.

References

  1. Alon, U., Barkai, N., Notterman, D., Gishdagger, K., Ybarradagger, S., Mackdagger, D., and Levine, A. (1999). Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Science of the USA, 96(12):745- 6750.
  2. Blake, C., Keogh, E., and Merz, C. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/ mlearn/MLRepository.html.
  3. Carpaneto, G. and Toth, P. (1980). Algorithm 548: solution of the assignment problem. ACM Transactions on Mathematical Software.
  4. Davidson, I., Wagstaff, K., and Basu, S. (2006). Measuring constraint-set utility for partitional clustering algorithms. In In proceedings of the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases 'PKDD0678, pages 115-126, Berlin, Germany.
  5. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., and Bloomfield, C. D. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286:531-537.
  6. He, X., Cai, D., and Niyogi, P. (2005). Laplacian score for feature selection. In Proceedings of the Advances in Neural Information Processing Systems ('NIPS 0578), pages 507-514, Vancouver, British Columbia, Canada.
  7. Kalakech, M., Porebski, A., Biela, P., Hamad, D., and Macaire, L. (2010). Constraint score for semisupervised selection of color texture features. In Proceedings of the third IEEE International Conference on Machine Vision (ICMV 2010), pages 275-279.
  8. Kudo, M. and Sklansky, J. (2000). Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1):25-4.
  9. Kulis, B., Basu, S., Dhillon, I., and Mooney, R. (2009). Semi-supervised graph clustering: a kernel approach. Machine Learning, 74(1):1-22.
  10. Liu, H. and Motoda, H. (1998). Feature extraction construction and selection a data mining perspective. Springer, first edition.
  11. Samaria, F. and Hartert, A. (1994). Parameterisation of a stochastic model for human face identification. In Proceedings of the Second IEEE Workshop on Applications of Computer Vision 'ACV 9478, pages 138-142, Sarasota, Florida.
  12. Sun, D. and Zhang, D. (2010). Bagging constraint score for feature selection with pairwise constraints. Pattern Recognition, 43:2106-2118.
  13. Wagstaff, K., Cardie, C., Rogers, S., and Schroedl, S. (2001). Constrained k-means clustering with background knowledge. In Proceedings of the Eighteenth International Conference on Machine Learning 'ICML 0178, pages 577-584, Williamstown, MA, USA.
  14. Zhang, D., Chen, S., and Zhou, Z. (2008). Constraint score: A new filter method for feature selection with pairwise constraints. Pattern Recognition, (41):1440-1451.
  15. Zhao, J., Lu, K., and He, X. (2008). Locality sensitive semisupervised feature selection. Neurocomputing, 71(10- 12):1842-1849.
  16. Zhao, Z. and Liu, H. (2007). Semi-supervised feature selection via spectral analysis. In Proceedings of the SIAM International Conference on Data Mining 'ICDM 0778, pages 641-646, Minneapolis.
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Paper Citation


in Harvard Style

Kalakech M., Biela P., Hamad D. and Macaire L. (2011). SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 175-182. DOI: 10.5220/0003680001750182


in Bibtex Style

@conference{ncta11,
author={Mariam Kalakech and Philippe Biela and Denis Hamad and Ludovic Macaire},
title={SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={175-182},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003680001750182},
isbn={978-989-8425-84-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - SEMI-SUPERVISED EVALUATION OF CONSTRAINT SCORES FOR FEATURE SELECTION
SN - 978-989-8425-84-3
AU - Kalakech M.
AU - Biela P.
AU - Hamad D.
AU - Macaire L.
PY - 2011
SP - 175
EP - 182
DO - 10.5220/0003680001750182