A novel ensemble method for k-nearest neighbor

Dietterich, 1997, Machine learning research: four current directions, AI Mag., 18, 97

Zhou, 2005, Ensembling local learners through multimodal perturbation, IEEE Trans. Syst. Man, Cybern. Part B Cybern., 35, 725, 10.1109/TSMCB.2005.845396

Ho, 1998, The random subspace method for constructing decision forests, IEEE Trans. Pattern Anal. Mach. Intell., 20, 832, 10.1109/34.709601

Ho, 1998, Nearest neighbors in random subspaces, 1451, 640

Gu, 2018, Random subspace based ensemble sparse representation, Pattern Recognit., 74, 544, 10.1016/j.patcog.2017.09.016

Rodríguez, 2006, Rotation forest: a new classifier ensemble method, IEEE Trans. Pattern Anal. Mach. Intell., 28, 1619, 10.1109/TPAMI.2006.211

Kuncheva, 2007, An experimental study on rotation forest ensembles, 459

Siedlecki, 1989, A note on genetic algorithms for large-scale feature selection, Pattern Recognit. Lett., 10, 335, 10.1016/0167-8655(89)90037-8

Breiman, 1996, Bagging predictors, Mach. Learn., 24, 123, 10.1007/BF00058655

Freund, 1997, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci., 55, 119, 10.1006/jcss.1997.1504

Zhang, 2000, Neural networks for classification: a survey, IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.), 30, 451, 10.1109/5326.897072

Gabrys, 2006, Genetic algorithms in classifier fusion, Appl. Soft Comput., 6, 337, 10.1016/j.asoc.2005.11.001

Kocev, 2013, Tree ensembles for predicting structured outputs, Pattern Recognit., 46, 817, 10.1016/j.patcog.2012.09.023

Tian, 2012, Coevolutionary learning of neural network ensemble for complex classification tasks, Pattern Recognit., 45, 1373, 10.1016/j.patcog.2011.09.012

Bao, 2004, Combining multiple k-nearest neighbor classifiers using different distance functions, 3177, 634

Ishii, 2005, Combining classification improvements by ensemble processing, 240

Altinçay, 2007, Ensembling evidential k-nearest neighbor classifiers through multi-modal perturbation, Appl. Soft Comput., 7, 1072, 10.1016/j.asoc.2006.10.002

Nanni, 2009, Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier, Neural Comput. Appl., 18, 105, 10.1007/s00521-007-0162-2

Pawlak, 1982, Rough sets, Int. J. Comput. Inf. Sci., 11, 341, 10.1007/BF01001956

Yao, 1998, Relational interpretations of neighborhood operators and rough set approximation operators, Inf. Sci., 111, 239, 10.1016/S0020-0255(98)10006-3

Hu, 2008, Neighborhood classifiers, Expert Syst. Appl., 34, 866, 10.1016/j.eswa.2006.10.043

Hu, 2008, Neighborhood rough set based heterogeneous feature subset selection, Inf. Sci., 178, 3577, 10.1016/j.ins.2008.05.024

Denoeux, 1995, A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Trans. Syst. Man. Cybern., 25, 804, 10.1109/21.376493

Groenen, 2001, Fuzzy clustering with squared Minkowski distances, Fuzzy Sets Syst., 120, 227, 10.1016/S0165-0114(98)00403-5

Stanfill, 1986, Toward memory-based reasoning, ACM Commun., 29, 1213, 10.1145/7902.7906

Wilson, 1997, Improved heterogeneous distance functions, J. Artif. Intell. Res., 6, 1, 10.1613/jair.346

Huang, 2005, Automated variable weighting in k-means type clustering, IEEE Trans. Pattern Anal. Mach. Intell., 27, 657, 10.1109/TPAMI.2005.95

Lian, 2015, An evidential classifier based on feature selection and two-step classification strategy, Pattern Recognit., 48, 2318, 10.1016/j.patcog.2015.01.019

Kleinberg, 1990, Stochastic discrimination, Ann. Math. Artif. Intell., 1, 207, 10.1007/BF01531079

Luo, 2013, Boosting the k-nearest-neighborhood based incremental collaborative filtering, Knowl.-Based Syst., 53, 90, 10.1016/j.knosys.2013.08.016

Calvo-Zaragoza, 2015, Improving kNN multi-label classification in Prototype selection scenarios using class proposals, Pattern Recognit., 48, 1608, 10.1016/j.patcog.2014.11.015

Prasartvit, 2013, Reducing bioinformatics data dimension with ABC-kNN, Neurocomputing, 116, 367, 10.1016/j.neucom.2012.01.045

García-Pedrajas, 2009, Boosting k-nearest neighbor classifier by means of input space projection, Expert Syst. Appl., 36, 10570, 10.1016/j.eswa.2009.02.065

Lichman

Kuncheva, 2014

Maclin, 1999, Popular ensemble methods: an empirical study, J. Artif. Intell. Res., 11, 169, 10.1613/jair.614

Demšar, 2006, Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1

Liu, 2008, A weighted rough set based method developed for class imbalance learning, Inf. Sci., 178, 1235, 10.1016/j.ins.2007.10.002

Pillai, 2017, Designing multi-label classifiers that maximize F-measures: state of the art, Pattern Recognit., 61, 394, 10.1016/j.patcog.2016.08.008

Cavalcanti, 2016, Combining diversity measures for ensemble pruning, Pattern Recognit. Lett., 74, 38, 10.1016/j.patrec.2016.01.029

Liang, 2018, Efficient multi-modal geometric mean metric learning, Pattern Recognit., 75, 1339, 10.1016/j.patcog.2017.02.032

Zhai, 2018, Parametric local multiview hamming distance metric learning, Pattern Recognit., 75, 250, 10.1016/j.patcog.2017.06.018

Lv, 2017, Metric learning via feature weighting for scalable image retrieval, Pattern Recognit. Lett., 109, 97, 10.1016/j.patrec.2017.09.026

Nguyen, 2017, Supervised distance metric learning through maximization of the Jeffrey divergence, Pattern Recognit., 64, 215, 10.1016/j.patcog.2016.11.010

Wang, 2017, Multi-view metric learning based on KL-divergence for similarity measurement, Neurocomputing, 238, 269, 10.1016/j.neucom.2017.01.062