Cite

Abdallah, L. and Shimshoni, I. (2013). An ensemble-clustering-based distance metric and its applications, International Journal of Business Intelligence and Data Mining 8(3): 264-287.10.1504/IJBIDM.2013.059052Search in Google Scholar

Abdallah, L. and Shimshoni, I. (2014). Mean shift clustering algorithm for data with missing values, 14th International Conference of DaWaK, Munich, Germany, pp. 426-438.Search in Google Scholar

Abdallah, L. and Shimshoni, I. (2016). k-means over incomplete datasets using mean Euclidean distance, 12th International Conference on Machine Learning and Data Mining, New York, NY, pp. 113-127.Search in Google Scholar

Bai, X., Zhang, M., Wu, Q., Zheng, R., Zhao, H. and Wei, W. (2015). A novel data filling algorithm for incomplete information system based on valued limited tolerance relation, International Journal of Database Theory and Application 8(6): 149-164.10.14257/ijdta.2015.8.6.14Search in Google Scholar

Clark, P.G., Grzymala-Busse, J.W. and Rzasa, W. (2013). Consistency of incomplete data, 2nd International Conference on Data Technologies and Applications, Marrakech, Morocco, pp. 80-87.Search in Google Scholar

Clustering datasets (2008). http://cs.joensuu.fi/sipu/datasets/, University of Eastern Finland, Joensuu.Search in Google Scholar

Dasgupta, S. and Hsu, D. (2008). Hierarchical sampling for active learning, 25th International Conference on Machine Learning, Helsinki, Finland, pp. 208-215.Search in Google Scholar

Dekel, O., Gentile, C. and Sridharan, K. (2012). Selective sampling and active learning from single and multiple teachers, Journal of Machine Learning Research 13(1): 2655-2697.Search in Google Scholar

Donders, A.R.T., van der Heijden, G.J., Stijnen, T. and Moons, K.G. (2006). Review: A gentle introduction to imputation of missing values, Journal of Clinical Epidemiology 59(10): 1087-1091.10.1016/j.jclinepi.2006.01.01416980149Search in Google Scholar

Grzymala-Busse, J. and Hu, M. (2001). A comparison of several approaches to missing attribute values in data mining, in W. Ziarko et al. (Eds.), Rough Sets and Current Trends in Computing, Springer, Berlin/Heidelberg, pp. 378-385.10.1007/3-540-45554-X_46Search in Google Scholar

Grzymala-Busse, J.W. (2006). A rough set approach to data with missing attribute values, in J.F. Peters and Y. Yao (Eds.), Rough Sets and Knowledge Technology, Springer, Berlin/Heidelberg, pp. 58-67.10.1007/11795131_10Search in Google Scholar

Hospedales, T.M., Gong, S. and Xiang, T. (2013). Finding rare classes: Active learning with generative and discriminative models, IEEE Transactions on Knowledge and Data Engineering 25(2): 374-386.10.1109/TKDE.2011.231Search in Google Scholar

Ibrahim, J.G., Chen, M.-H., Lipsitz, S.R. and Herring, A.H. (2005). Missing-data methods for generalized linear models: A comparative review, Journal of the American Statistical Association 100(469): 332-346.10.1198/016214504000001844Search in Google Scholar

Lewis, D. and Gale, W. (1994). A sequential algorithm for training text classifiers, 17th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, Dublin, Ireland, pp. 3-12.Search in Google Scholar

Li, H., Shi, Y., Liu, Y., Hauptmann, A.G. and Xiong, Z. (2012). Cross-domain video concept detection: A joint discriminative and generative active learning approach, Expert Systems with Applications 39(15): 12220-12228.10.1016/j.eswa.2012.04.054Search in Google Scholar

Lindenbaum, M., Markovitch, S. and Rusakov, D. (2004). Selective sampling for nearest neighbor classifiers, Machine Learning 54(2): 125-152.10.1023/B:MACH.0000011805.60520.feSearch in Google Scholar

Little, R.J. (1988). Missing-data adjustments in large surveys, Journal of Business & Economic Statistics 6(3): 287-296.10.1080/07350015.1988.10509663Search in Google Scholar

Little, R.J. and Rubin, D.B. (2014). Statistical Analysis with Missing Data, John Wiley & Sons. Hoboken, NJ.Search in Google Scholar

Lughofer, E. (2012). Hybrid active learning for reducing the annotation effort of operators in classification systems, Pattern Recognition 45(2): 884-896.10.1016/j.patcog.2011.08.009Search in Google Scholar

MacQueen, J.B. (1967). Some methods for classification and analysis of multivariate observations, 5th Symposium on Math, Statistics, and Probability, Berkeley, CA, USA, pp. 281-297.Search in Google Scholar

Magnani, M. (2004). Techniques for dealing with missing data in knowledge discovery tasks, Obtido 15(01): 2007.Search in Google Scholar

Nowicki, R.K. (2010). On classification with missing data using rough-neuro-fuzzy systems, International Journal of Applied Mathematics and Computer Science 20(1): 55-67, DOI: 10.2478/v10006-010-0004-8.10.2478/v10006-010-0004-8Search in Google Scholar

Nowicki, R.K., Nowak, B.A. and Woźniak, M. (2016). Application of rough sets in k nearest neighbours algorithm for classification of incomplete samples, in S. Kunifuji et al. (Eds.), Knowledge, Information and Creativity Support Systems, Springer, Berlin/Heidelberg, pp. 243-257.10.1007/978-3-319-27478-2_17Search in Google Scholar

Stefanowski, J. and Tsoukias, A. (2001). Incomplete information tables and rough classification, Computational Intelligence 17(3): 545-566.10.1111/0824-7935.00162Search in Google Scholar

Strehl, A. and Ghosh, J. (2002). Cluster ensembles-A knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research 3: 583-617.Search in Google Scholar

Tan, M. and Schlimmer, J. (1990). Two case studies in cost-sensitive concept acquisition, 8th National Conference on Artificial Intelligence, Boston, MA, USA, pp. 854-860.Search in Google Scholar

Turney, P. (1995). Cost-sensitive classification: Empirical evaluation of a hybrid genetic decision tree induction algorithm, Journal of Artificial Intelligence Research 2(1): 369-409.10.1613/jair.120Search in Google Scholar

Xu, Z., Akella, R. and Zhang, Y. (2007). Incorporating diversity and density in active learning for relevance feedback, in G. Amati et al. (Eds.), Advances in Information Retrieval, Springer, Berlin/Heidelberg, pp. 246-257.10.1007/978-3-540-71496-5_24Search in Google Scholar

Zhang, S., Qin, Z., Ling, C. and Sheng, S. (2005). Missing is useful: Missing values in cost-sensitive decision trees, IEEE Transactions on Knowledge and Data Engineering 17(12): 1689-1693.10.1109/TKDE.2005.188Search in Google Scholar

Zhang, Y., Wen, J., Wang, X. and Jiang, Z. (2014). Semi-supervised learning combining co-training with active learning, Expert Systems with Applications 41(5): 2372-2378.10.1016/j.eswa.2013.09.035Search in Google Scholar

eISSN:
2083-8492
Idioma:
Inglés
Calendario de la edición:
4 veces al año
Temas de la revista:
Mathematics, Applied Mathematics