B. Calvo, N. López-bigas, S. J. Furney, P. Larrañaga, and J. A. Lozano, A partially supervised classification approach to dominant and recessive human disease gene prediction, Computer Methods and Programs in Biomedicine, vol.85, issue.3, pp.229-237, 2007.
DOI : 10.1016/j.cmpb.2006.12.003

H. Yu, J. Han, and K. C. Chang, PEBL, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.239-248, 2002.
DOI : 10.1145/775047.775083

M. A. Zuluaga, D. Hush, E. J. Leyton, M. H. Hoyos, and M. Orkisz, Learning from Only Positive and Unlabeled Data to Detect Lesions in Vascular CT Images, Proceedings of MICCAI 2011, pp.9-16, 2011.
DOI : 10.1007/978-3-642-18421-5_14

F. D. Comité, F. Denis, R. Gilleron, and F. Letouzey, Positive and unlabeled examples help learning, Proceedings of ALT 1999, pp.219-230, 1999.

S. Boriah, V. Chandola, and V. Kumar, Similarity Measures for Categorical Data: A Comparative Evaluation, Proceedings of SDM 2008, pp.243-254, 2008.
DOI : 10.1137/1.9781611972788.22

D. Ienco, R. G. Pensa, and R. Meo, From Context to Distance, ACM Transactions on Knowledge Discovery from Data, vol.6, issue.1, pp.1-125, 2012.
DOI : 10.1145/2133360.2133361

L. Yu and H. Liu, Feature selection for high-dimensional data: A fast correlation-based filter solution, Proceedings of ICML 2003, AAAI, pp.856-863, 2003.

C. Elkan and K. Noto, Learning classifiers from only positive and unlabeled data, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.213-220, 2008.
DOI : 10.1145/1401890.1401920

Y. Xiao, B. Liu, J. Yin, L. Cao, C. Zhang et al., Similarity-based approach for positive and unlabeled learning, Proceedings of IJCAI 2011, pp.1577-1582, 2011.

K. Zhou, G. Xue, Q. Yang, and Y. Yu, Learning with Positive and Unlabeled Examples Using Topic-Sensitive PLSA, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.1, pp.46-58, 2010.
DOI : 10.1109/TKDE.2009.56

F. Mordelet and J. Vert, A bagging SVM to learn from positive and unlabeled examples, Pattern Recognition Letters, vol.37, pp.201-209, 2014.
DOI : 10.1016/j.patrec.2013.06.010
URL : https://hal.archives-ouvertes.fr/hal-01101852

J. Wu, X. Zhu, C. Zhang, and Z. Cai, Multi-Instance Learning from Positive and Unlabeled Bags, Proceedings of PAKDD 2014, pp.237-248, 2014.
DOI : 10.1007/978-3-319-06608-0_20

H. Li, Z. Chen, B. Liu, X. Wei, and J. Shao, Spotting Fake Reviews via Collective Positive-Unlabeled Learning, 2014 IEEE International Conference on Data Mining, pp.899-904, 2014.
DOI : 10.1109/ICDM.2014.47

P. Yang, X. Li, H. Chua, C. Kwoh, and S. Ng, Ensemble Positive Unlabeled Learning for Disease Gene Identification, PLoS ONE, vol.29, issue.2
DOI : 10.1371/journal.pone.0097079.s005

B. Calvo, P. Larrañaga, and J. A. Lozano, Learning Bayesian classifiers from positive and unlabeled examples, Pattern Recognition Letters, vol.28, issue.16, pp.2375-2384, 2007.
DOI : 10.1016/j.patrec.2007.08.003

N. Friedman, D. Geiger, and M. Goldszmidt, Bayesian network classifiers, Machine Learning, vol.29, issue.2/3, pp.131-163, 1997.
DOI : 10.1023/A:1007465528199

J. He, Y. Zhang, X. Li, and Y. Wang, Naive Bayes Classifier for Positive Unlabeled Learning with Uncertainty, Proceedings of SDM 2010, pp.361-372, 2010.
DOI : 10.1137/1.9781611972801.32

Y. Zhao, X. Kong, and P. S. Yu, Positive and Unlabeled Learning for Graph Classification, 2011 IEEE 11th International Conference on Data Mining, pp.962-971, 2011.
DOI : 10.1109/ICDM.2011.119

Y. Shao, W. Chen, L. Liu, and N. Deng, Laplacian unit-hyperplane learning from positive and unlabeled examples, Information Sciences, vol.314, pp.314-152, 2015.
DOI : 10.1016/j.ins.2015.03.066

B. Liu, W. S. Lee, P. S. Yu, and X. Li, Partially supervised classification of text documents, Proceedings of ICML 2002, pp.387-394, 2002.

V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection, ACM Computing Surveys, vol.41, issue.3
DOI : 10.1145/1541880.1541882

B. S. John, J. C. Platt, J. Shawe-taylor, A. J. Smola, and R. C. Williamson, Estimating the support of a high-dimensional distribution, Neural Computation, vol.13, 1999.

A. Blum and T. M. Mitchell, Combining labeled and unlabeled sata with co-training, Proceedings of COLT 1998, pp.92-100, 1998.

M. C. Du-plessis and M. Sugiyama, Semi-supervised learning of class balance under class-prior change by distribution matching, Neural Networks, vol.50, pp.110-119, 2014.
DOI : 10.1016/j.neunet.2013.11.010

H. Gan, R. Huang, Z. Luo, Y. Fan, and F. Gao, Towards a probabilistic semi-supervised Kernel Minimum Squared Error algorithm, Neurocomputing, vol.171
DOI : 10.1016/j.neucom.2015.06.031

S. Basu, A. Banerjee, and R. J. Mooney, Semi-supervised clustering by seeding, Proceedings of ICML 2002, pp.27-34, 2002.

I. Davidson and S. Ravi, The complexity of non-hierarchical clustering with instance and cluster level constraints, Data Mining and Knowledge Discovery, vol.2, issue.3, pp.25-61, 2007.
DOI : 10.1007/s10618-006-0053-7

M. Bilenko, S. Basu, and R. J. Mooney, Integrating constraints and metric learning in semi-supervised clustering, Twenty-first international conference on Machine learning , ICML '04, pp.81-88, 2004.
DOI : 10.1145/1015330.1015360

L. Ma, X. Yang, and D. Tao, Person re-identification over camera networks using multi-task distance metric learning, IEEE Transactions on Image Processing, vol.23, issue.8, pp.3656-3670, 2014.

Y. Luo, T. Liu, D. Tao, and C. Xu, Decomposition-Based Transfer Distance Metric Learning for Image Classification, IEEE Transactions on Image Processing, vol.23, issue.9, pp.3789-3801, 2014.
DOI : 10.1109/TIP.2014.2332398

J. Yu, D. Tao, J. Li, and J. Cheng, Semantic preserving distance metric learning and applications, Information Sciences, vol.281, pp.674-686, 2014.
DOI : 10.1016/j.ins.2014.01.025

M. Ring, F. Otto, M. Becker, T. Niebler, D. Landes et al., ConDist: A Context-Driven Categorical Distance Measure, Proceedings of ECML PKDD 2015, pp.251-266, 2015.
DOI : 10.1007/978-3-319-23528-8_16

R. J. Quinlan and C. , 5: Programs for Machine Learning, Machine Learning, 1993.

S. Kullback and R. A. Leibler, On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.49-86, 1951.
DOI : 10.1214/aoms/1177729694

J. Demsar, Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research, vol.7, pp.1-30, 2006.