Feature subset selection from positive and unlabelled examples

Pattern Recognition Letters

Volumn 30, Issue 11, 2009, Pages 1027-1036

  Calvo, Borja ^a   Larrañaga, Pedro ^b   Lozano, Jose A ^a  

^a UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^b UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Feature subset selection; Filter methods; Partially supervised classification; Positive unlabelled learning

Indexed keywords

BAYESIAN NETWORK MODELS; FEATURE SUBSET SELECTION; FILTER METHODS; FILTER SELECTION; LEARNING CONTEXT; MACHINE LEARNING APPLICATIONS; NEGATIVE EXAMPLES; PARTIALLY SUPERVISED CLASSIFICATION; POSITIVE EXAMPLES; POSITIVE UNLABELLED LEARNING; REAL-LIFE DATABASE; SYNTHETIC DATASETS;

ALGORITHMS; BAYESIAN NETWORKS; EDUCATION; INFERENCE ENGINES;

SET THEORY;

EID: 67649094096     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2009.04.015     Document Type: Article

References (26)

1. Amaldi E., and Kann V. On the approximability of minimizing nonzero variables or unsatisfied relations in linear systems. Theor. Comput. Sci. 209 1-2 (1998) 237-260
2. Ben-Bassat M. Use of distance measures, information measures and error bounds in feature evaluation. In: Krishnaiah P.R., and Kanal L.N. (Eds). Handbook of Statistics vol. 2 (1982), North-Holland Publishing Company 773-791
3. Bishop C.M. Pattern Recognition and Machine Learning (2006), Springer
4. Blake, C.L., Merz, C.J., 1998. UCI Repository of Machine Learning Databases. .
5. Calvo B., Larrañaga P., and Lozano J.A. Learning Bayesian classifiers from positive and unlabeled examples. Pattern Recognit. Lett. 28 16 (2007) 2375-2384
6. Calvo B., López-Bigas N., Furney S.J., Larrañaga P., and Lozano J.A. A partially supervised classification approach to dominant and recessive human disease gene prediction. Comput. Meth. Prog. Biomed. 85 3 (2007) 229-237
7. Castelo R., and Guigó R. Splice site identification by idlbns. Bioinformatics 4 20 (2004) 69-76
8. Cover T.M., and Thomas J.A. Elements of Information Theory. second ed. (2006), John Wiley & Sons Inc
9. Denis, F., Gilleron, R., Tommasi, M., 2002. Text classification from positive and unlabeled examples. In: The 9th Internat. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2002, pp. 1927-1934.
10. Denis, F., Laurent, A., Gilleron, R., Tommasi, M., 2003. Text classification and co-training from positive and unlabeled examples. In: Proc. ICML 2003 Workshop: The Continuum from Labeled to Unlabeled Data, pp. 80-87.
11. Duda R.O., Hart P.E., and Stork D.G. Pattern Classification (2001), Wiley Interscience
12. Efron B. Estimating the error rate of a prediction rule: Improvement on cross-validation. J. Amer. Statist. Assoc. 78 (1983) 316-331
13. Efron B., and Tibshirani R.J. An Introduction to the Bootstrap (1993), Chapman & Hall
14. Friedman N., Geiger D., and Goldszmit M. Bayesian network classifiers. Mach. Learn. 29 (1997) 131-163
15. Furney S.J., Calvo B., Larrañaga P., Lozano J.A., and Lopez-Bigas N. Prioritization of candidate cancer genes - an aid to oncogenomic studies. Nucleic Acids Res. 36 18 (2008) e115
16. Guyon I., and Elisseeff A. An introduction to variable and feature selection. J. Mach. Learn. Res. 3 (2003) 1157-1182
17. Hall, M.A., Smith, L.A., 1997. Feature subset selection: A correlation based filter approach. In: Springer (Ed.), Proc. Internat. Conf. on Neural Information Processing and Intelligent Information Systems, pp. 855-858.
18. Inza I., Larrañaga P., Etxeberria R., and Sierra B. Feature subset selection by Bayesian networks based optimization. Artif. Intell. 123 1-2 (2000) 157-184
19. Kohavi R., and John G.H. Wrappers for feature subset selection. Artif. Intell. 97 1-2 (1997) 273-324
20. Liu, B., Dai, Y., Li, X., Lee, W.S., Yu, P.S., 2003. Building text classifiers using positive and unlabeled examples. In: Third IEEE Internat. Conf. on Data Mining (ICDM'03), pp. 179-186.
21. Liu, B., Lee, W.S., Yu, P.S., Li, X., 2002. Partially supervised classification of text documents. In: Proc. 19th Internat. Conf. on Machine Learning, pp. 387-394.
22. Liu H., and Motoda H. Feature Extraction, Construction and Selection: A Data Mining Perspective (1998), Kluwer Academic Publishers
23. In: Liu H., and Motoda H. (Eds). Computational Methods of Feature Selection (2008), Chapman and Hall/CRC Press
24. Minsky M. Steps toward artificial intelligence. Proc. Inst. Radio Eng. 49 (1961) 8-30
25. Quinlan J.R. C4.5: Programs for Machine Learning (1993), Morgan Kaufmann Publishers
26. Saeys Y., Inza I., and Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 23 (2007) 2507-2517