Open Access

Ensembles of instance selection methods: A comparative study

International Journal of Applied Mathematics and Computer Science's Cover Image
International Journal of Applied Mathematics and Computer Science
Exploring Complex and Big Data (special section, pp. 7-91), Johann Gamper, Robert Wrembel (Eds.)

Cite

Abdi, H. (2010). Holm’s sequential Bonferroni procedure, Encyclopedia of Research Design1(8): 620–627.Search in Google Scholar

Aha, D., Kibler, D. and Albert, M. (1991). Instance-based learning algorithms, Machine Learning6(1): 37–66.10.1007/BF00153759Search in Google Scholar

Alcalá-Fdez, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sanchez, L. and Herrera, F. (2011). Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework, Journal of Multiple-Valued Logic & Soft Computing17: 255–287.Search in Google Scholar

Arnaiz-González, Á., Blachnik, M., Kordos, M. and García-Osorio, C. (2016a). Fusion of instance selection methods in regression tasks, Information Fusion30: 69–79.10.1016/j.inffus.2015.12.002Search in Google Scholar

Arnaiz-González, Á., Díez-Pastor, J., Rodríguez, J.J. and García-Osorio, C.I. (2016b). Instance selection for regression: Adapting DROP, Neurocomputing201: 66–81.10.1016/j.neucom.2016.04.003Search in Google Scholar

Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning36(1): 105–139.10.1023/A:1007515423169Search in Google Scholar

Bezdek, J.C., Ehrlich, R. and Full, W. (1984). FCM: The fuzzy C-means clustering algorithm, Computers & Geosciences10(2–3): 191–203.10.1016/0098-3004(84)90020-7Search in Google Scholar

Bhattacharya, B., Poulsen, R. and Toussaint, G. (1984). Application of proximity graphs to editing nearest neighbor decision rules, International Symposium on Information Theory, Santa Monica, CA, USA, pp. 97–108.Search in Google Scholar

Blachnik, M. (2014). Ensembles of instance selection methods based on feature subset, IEEE Procedia Computer Science35: 388–396.10.1016/j.procs.2014.08.119Search in Google Scholar

Blachnik, M. and Kordos, M. (2014). Bagging of instance selection algorithms, International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, pp. 40–51.10.1007/978-3-319-07176-3_4Search in Google Scholar

Brighton, H. and Mellish, C. (2002). Advances in instance selection for instance-based learning algorithms, Data Mining and Knowledge Discovery6(2): 153–172.10.1023/A:1014043630878Search in Google Scholar

Czarnowski, I. and Jędrzejowicz, P. (2015). Ensemble online classifier based on the one-class base classifiers for mining data streams, Cybernetics and Systems46(1–2): 51–68.10.1080/01969722.2015.1007736Search in Google Scholar

Freund, Y. and Schapire, R.E. (1996). Experiments with a new boosting algorithm, International Conference on Machine Learning, Bari, Italy, pp. 148–156.Search in Google Scholar

Freund, Y. and Schapire, R.E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences55(1): 119–139.10.1006/jcss.1997.1504Search in Google Scholar

Galar, M., Fernández, A., Barrenechea, E., Bustince, H. and Herrera, F. (2011). An overview of ensemble methods for binary classifiers in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes, Pattern Recognition44(8): 1761–1776.10.1016/j.patcog.2011.01.017Search in Google Scholar

García-Osorio, C., de Haro-García, A. and García-Pedraja, N. (2010). Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts, Artificial Intelligence174(4–5): 410–441.10.1016/j.artint.2010.01.001Search in Google Scholar

García, S., Derrac, J., Cano, J.R. and Herrera, F. (2012). Prototype selection for nearest neighbor classification: Taxonomy and empirical study, IEEE Transactions on Pattern Analysis and Machine Intelligence34(3): 417–435.10.1109/TPAMI.2011.14221768651Search in Google Scholar

García-Pedrajas, N. (2009). Constructing ensembles of classifiers by means of weighted instance selection, IEEE Transactions on Neural Networks20(2): 258–277.10.1109/TNN.2008.200549619179252Search in Google Scholar

García-Pedrajas, N. and De Haro-García, A. (2014). Boosting instance selection algorithms, Knowledge-Based Systems67: 342–360.10.1016/j.knosys.2014.04.021Search in Google Scholar

García, S., Luengo, J. and Herrera, F. (2016). Tutorial on practical tips of the most influential data preprocessing algorithms in data mining, Knowledge-Based Systems98: 1–29.10.1016/j.knosys.2015.12.006Search in Google Scholar

Grochowski, M. and Jankowski, N. (2004). Comparison of instance selection algorithms. II: Results and comments, Lecture Notes in Computer Science, Vol. 3070, pp. 580–585.10.1007/978-3-540-24844-6_87Search in Google Scholar

Gunn, I.A., Arnaiz-González, Á. and Kuncheva, L.I. (2018). A taxonomic look at instance-based stream classifiers, Neurocomputing286: 167–178.10.1016/j.neucom.2018.01.062Search in Google Scholar

Hart, P. (1968). The condensed nearest neighbor rule, IEEE Transactions on Information Theory14(3): 515–516.10.1109/TIT.1968.1054155Search in Google Scholar

Herrera, F. (2005). Keel, knowledge extraction based on evolutionary learning, Spanish National Projects TIC2002-04036-C05, TIN2005-08386-C05 and TIN2008-06681-C06, http://www.keel.es.Search in Google Scholar

Jacobs, R.A., Jordan, M.I., Nowlan, S.J. and Hinton, G.E. (1991). Adaptive mixtures of local experts, Neural Computation3(1): 79–87.10.1162/neco.1991.3.1.7931141872Search in Google Scholar

Jankowski, N. and Grochowski, M. (2004). Comparison of instance selection algorithms. I: Algorithms survey, International Conference on Artificial Intelligence and Soft Computing, Zakopane, Poland, Vol. 3070, pp. 598–603.10.1007/978-3-540-24844-6_90Search in Google Scholar

Kordos, M. and Blachnik, M. (2012). Instance selection with neural networks for regression problems, International Conference on Artificial Neural Networks, Lausanne, Switzerland, pp. 263–270.10.1007/978-3-642-33266-1_33Search in Google Scholar

Kordos, M. and Rusiecki, A. (2016). Reducing noise impact on MLP training, Soft Computing20(1): 49–65.10.1007/s00500-015-1690-9Search in Google Scholar

Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms, Wiley, Hoboken, NJ.10.1002/0471660264Search in Google Scholar

Kuncheva, L.I., Bezdek, J.C. and Duin, R.P. (2001). Decision templates for multiple classifier fusion: An experimental comparison, Pattern Recognition34(2): 299–314.10.1016/S0031-3203(99)00223-XSearch in Google Scholar

Marchiori, E. (2008). Hit miss networks with applications to instance selection, Journal of Machine Learning Research9(Jun): 997–1017.Search in Google Scholar

Marchiori, E. (2010). Class conditional nearest neighbor for large margin instance selection, IEEE Transactions on Pattern Analysis and Machine Intelligence32(2): 364–370.10.1109/TPAMI.2009.16420075464Search in Google Scholar

Raviv, Y. and Intrator, N. (1996). Bootstrapping with noise: An effective regularization technique, Connection Science8(3–4): 355–372.10.1080/095400996116811Search in Google Scholar

Rokach, L. (2009). Taxonomy for characterizing ensemble methods in classification tasks: A review and annotated bibliography, Computational Statistics & Data Analysis53(12): 4046–4072.10.1016/j.csda.2009.07.017Search in Google Scholar

Schapire, R.E. (1990). The strength of weak learnability, Machine Learning5(2): 197–227.10.1007/BF00116037Search in Google Scholar

Sebban, M., Nock, R. and Lallich, S. (2002). Stopping criterion for boosting-based data reduction techniques: From binary to multiclass problem, Journal of Machine Learning Research3(Dec): 863–885.Search in Google Scholar

Shaker, A. and Hüllermeier, E. (2012). IBLStreams: A system for instance-based classification and regression on data streams, Evolving Systems3(4): 235–249.10.1007/s12530-012-9059-0Search in Google Scholar

Skurichina, M. and Duin, R.P. (2001). Bagging and the random subspace method for redundant feature spaces, International Workshop on Multiple Classifier Systems, Cagliari, Italy, pp. 1–10.10.1007/3-540-48219-9_1Search in Google Scholar

Song, Y., Liang, J., Lu, J. and Zhao, X. (2017). An efficient instance selection algorithm for k nearest neighbor regression, Neurocomputing251: 26–34.10.1016/j.neucom.2017.04.018Search in Google Scholar

Tomek, I. (1976). An experiment with the edited nearest-neighbor rule, IEEE Transactions on Systems, Man, and Cybernetics6: 448–452.10.1109/TSMC.1976.4309523Search in Google Scholar

Wilson, D. (1972). Asymptotic properties of nearest neighbor rules using edited data, IEEE Transactions Systems, Man and Cybernetics2: 408–421.10.1109/TSMC.1972.4309137Search in Google Scholar

Wilson, D. and Martinez, T. (2000). Reduction techniques for instance-based learning algorithms, Machine Learning38(3): 257–268.10.1023/A:1007626913721Search in Google Scholar

Wolpert, D.H. (1992). Stacked generalization, Neural Networks5(2): 241–259.10.1016/S0893-6080(05)80023-1Search in Google Scholar

Woźniak, M., Graña, M. and Corchado, E. (2014). A survey of multiple classifier systems as hybrid systems, Information Fusion16: 3–17.10.1016/j.inffus.2013.04.006Search in Google Scholar

Zhu, J., Zou, H., Rosset, S. and Hastie, T. (2009). Multi-class AdaBoost, Statistics and Its Interface2(3): 349–360.10.4310/SII.2009.v2.n3.a8Search in Google Scholar

eISSN:
2083-8492
Language:
English
Publication timeframe:
4 times per year
Journal Subjects:
Mathematics, Applied Mathematics