KEEL: a software tool to assess evolutionary algorithms for data mining problems, Soft Computing, vol.17, issue.2, pp.307-318, 2009. ,
DOI : 10.1007/s00500-008-0323-y
Advances in instance selection for instance-based learning algorithms, Data Mining and Knowledge Discovery, vol.6, issue.2, pp.153-172, 2002. ,
DOI : 10.1023/A:1014043630878
A sample set condensation algorithm for the class sensitive artificial neural network, Pattern Recognition Letters, vol.17, issue.8, pp.819-823, 1996. ,
DOI : 10.1016/0167-8655(96)00041-4
Nearest neighbor (NN) norms : NN pattern classification techniques, 1991. ,
Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.3, pp.417-435, 2012. ,
DOI : 10.1109/TPAMI.2011.142
Comparison of Instance Selection Algorithms II. Results and Comments, In: Artificial Intelligence and Soft Computing -ICAISC Lecture Notes in Computer Science, vol.3070, pp.580-585, 2004. ,
DOI : 10.1007/978-3-540-24844-6_87
The condensed nearest neighbor rule (Corresp.), IEEE Transactions on Information Theory, vol.14, issue.3, pp.515-516, 1968. ,
DOI : 10.1109/TIT.1968.1054155
Clustering-Based Reference Set Reduction for k-Nearest Neighbor, 4th international symposium on Neural Networks: Part II?Advances in Neural Networks, pp.880-888, 2007. ,
DOI : 10.1007/978-3-540-72393-6_105
Comparison of Instances Seletion Algorithms I. Algorithms Survey, In: Artificial Intelligence and Soft Computing -ICAISC Lecture Notes in Computer Science, vol.3070, pp.598-603, 2004. ,
DOI : 10.1007/978-3-540-24844-6_90
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.3981
Cluster-Based Similarity Search in Time Series, 2009 Fourth Balkan Conference in Informatics, 2009. ,
DOI : 10.1109/BCI.2009.22
Some methods for classification and analysis of multivariate observations, Proc. of 5th Berkeley Symp. on Math. Statistics and Probability, pp.281-298, 1967. ,
A review of instance selection methods, Artificial Intelligence Review, vol.35, issue.9, pp.133-143, 2010. ,
DOI : 10.1007/s10462-010-9165-y
Efficient data-set size reduction by finding homogeneous clusters, Procendings of the fifth Balkan Conference in Informatics. p. to appear. BCI '12, 2012. ,
DOI : 10.1145/2371316.2371349
An Adaptive Hybrid and Cluster-Based Model for Speeding Up the k-NN Classifier, In: Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science, vol.7209, pp.163-175, 2012. ,
DOI : 10.1007/978-3-642-28931-6_16
Foundations of multidimensional and metric data structures. The Morgan Kaufmann series in computer graphics, 2006. ,
High training set size reduction by space partitioning and prototype abstraction, Pattern Recognition, vol.37, issue.7, pp.1561-1564, 2004. ,
DOI : 10.1016/j.patcog.2003.12.012
A Taxonomy and Experimental Study on Prototype Generation for Nearest Neighbor Classification, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol.42, issue.1, pp.86-100, 2012. ,
DOI : 10.1109/TSMCC.2010.2103939
A fast exact k-nearest neighbors algorithm for high dimensional search using k-means clustering and triangle inequality, The 2011 International Joint Conference on Neural Networks, pp.1293-1299, 2011. ,
DOI : 10.1109/IJCNN.2011.6033373
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3255306
Reduction techniques for instance-based learning algorithms, Machine Learning, vol.38, issue.3, pp.257-286, 2000. ,
DOI : 10.1023/A:1007626913721
Fast k-nearest neighbor classification using cluster-based trees, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.4, pp.525-528, 2004. ,
DOI : 10.1109/TPAMI.2004.1265868