Abstract
Monotonicity constraints frequently appear in real-life problems. Many of the monotonic classifiers used in these cases require that the input data satisfy the monotonicity restrictions. This contribution proposes the use of training set selection to choose the most representative instances which improves the monotonic classifiers performance, fulfilling the monotonic constraints. We have developed an experiment on 30 data sets in order to demonstrate the benefits of our proposal.
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Kotłowski, W., Słowiński, R.: On nonparametric ordinal classification with monotonicity constraints. IEEE Trans. Knowl. Data Eng. 25(11), 2576–2589 (2013)
Gutiérrez, P.A., García, S.: Current prospects on ordinal and monotonic classification. Prog. Artif. Intell. 5(3), 171–179 (2016)
Chen, C.C., Li, S.T.: Credit rating with a monotonicity-constrained support vector machine model. Expert Syst. Appl. 41(16), 7235–7247 (2014)
Ben-David, A.: Monotonicity maintenance in information theoretic machine learning algorithms. Mach. Learn. 19, 29–43 (1995)
Potharst, R., Bioch, J.: Decision trees for ordinal classification. Intell. Data Anal. 4, 97–111 (2000)
Alcalá-Fdez, J., Alcalá, R., González, S., Nojima, Y., García, S.: Evolutionary fuzzy rule-based methods for monotonic classification. IEEE Trans. Fuzzy Syst. 25(6), 1376–1390 (2017)
Duivesteijn, W., Feelders, A.: Nearest neighbour classification with monotonicity constraints. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 301–316. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87479-9_38
García, J., Albar, A., Aljohani, N., Cano, J.R., García, S.: Hyperrectangles selection for monotonic classification by using evolutionary algorithms. Int. J. Comput. Intell. Syst. 9(1), 184–201 (2016)
García, J., Fardoun, H.M., Alghazzawi, D.M., Cano, J.R., García, S.: Mongel: monotonic nested generalized exemplar learning. Pattern Anal. Appl. 20(2), 441–452 (2017)
Frénay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845–869 (2014)
Triguero, I., González, S., Moyano, J.M., García, S., Alcalá-Fdez, J., Luengo, J., Fernández, A., del Jesús, M.J., Sánchez, L., Herrera, F.: Keel 3.0: an open source software for multi-stage analysis in data mining. Int. J. Comput. Intell. Syst. 10(1), 1238–1249 (2017)
Feelders, A.: Monotone relabeling in ordinal classification. In: IEEE International Conference on Data Mining (ICDM), pp. 803–808 (2010)
García, S., Derrac, J., Cano, J.R., Herrera, F.: Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 417–435 (2012)
Silva, D.A., Souza, L.C., Motta, G.H.: An instance selection method for large datasets based on markov geometric diffusion. Data Knowl. Eng. 101, 24–41 (2016)
García, S., Luengo, J., Herrera, F.: Tutorial on practical tips of the most influential data preprocessing algorithms in data mining. Knowl. Based Syst. 98, 1–29 (2016)
Cano, J.R., Aljohani, N.R., Abbasi, R.A., Alowidbi, J.S., García, S.: Prototype selection to improve monotonic nearest neighbor. Eng. Appl. Artif. Intell. 60, 128–135 (2017)
Cano, J.R., Herrera, F., Lozano, M.: Stratification for scaling up evolutionary prototype selection. Pattern Recogn. Lett. 26(7), 953–963 (2005)
Cano, J.R., García, S., Herrera, F.: Subgroup discover in large size data sets preprocessed using stratified instance selection for increasing the presence of minority classes. Pattern Recogn. Lett. 29(16), 2156–2164 (2008)
García, S., Luengo, J., Herrera, F.: Data Preprocessing in Data Mining. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-319-10247-4
Cano, J.R., Herrera, F., Lozano, M.: On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Appl. Soft Comput. 6(3), 323–332 (2006)
Nanni, L., Lumini, A., Brahnam, S.: Weighted reward-punishment editing. Pattern Recogn. Lett. 75, 48–54 (2016)
Hu, Q., Che, X., Zhang, L., Zhang, D., Guo, M., Yu, D.: Rank entropy-based decision trees for monotonic classification. IEEE Trans. Knowl. Data Eng. 24(11), 2052–2064 (2012)
Alcalá, J., Fernández, A., Luengo, J., Derrac, J., García, S., Sánchez, L., Herrera, F.: Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework. J. Mult. Valued Logic Soft Comput. 17(255–287), 11 (2010)
Bache, K., Lichman, M.: UCI machine learning repository (2013)
Ben-David, A., Serling, L., Pao, Y.: Learning and classification of monotonic ordinal concepts. Comput. Intell. 5, 45–49 (1989)
Lievens, S., De Baets, B., Cao-Van, K.: A probabilistic framework for the design of instance-based supervised ranking algorithms in an ordinal setting. Ann. Oper. Res. 163, 115–142 (2008)
Lievens, S., De Baets, B.: Supervised ranking in the weka environment. Inf. Sci. 180(24), 4763–4771 (2010)
Gaudette, L., Japkowicz, N.: Evaluation methods for ordinal classification. In: Gao, Y., Japkowicz, N. (eds.) AI 2009. LNCS (LNAI), vol. 5549, pp. 207–210. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01818-3_25
Milstein, I., Ben-David, A., Potharst, R.: Generating noisy monotone ordinal datasets. Artif. Intell. Res. 3(1), 30–37 (2014)
Gibbons, J.D., Chakraborti, S.: Nonparametric statistical inference. In: Lovric, M. (ed.) International Encyclopedia of Statistical Science. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-04898-2_420
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Triguero, I., Peralta, D., Bacardit, J., García, S., Herrera, F.: Mrpr: a mapreduce solution for prototype reduction in big data classification. Neurocomputing 150, 331–345 (2015)
Acknowledgement
This work was supported by TIN2014-57251-P, by the Spanish “Ministerio de Economía y Competitividad” and by “Fondo Europeo de Desarrollo Regional” (FEDER) under Project TEC2015-69496-R and the Foundation BBVA project 75/2016 BigDaPTOOLS.
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Cano, JR., García, S. (2018). A First Attempt on Monotonic Training Set Selection. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_23
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DOI: https://doi.org/10.1007/978-3-319-92639-1_23
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