@article {CANO2019,
	title = {Label noise filtering techniques to improve monotonic classification},
	journal = {Neurocomputing},
	volume = {353},
	year = {2019},
	note = {TIN2014-57251-P; TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS},
	month = {08/2019},
	pages = {83-95},
	abstract = {The monotonic ordinal classification has increased the interest of researchers and practitioners within machine learning community in the last years. In real applications, the problems with monotonicity constraints are very frequent. To construct predictive monotone models from those problems, many classifiers require as input a data set satisfying the monotonicity relationships among all samples. Changing the class labels of the data set (relabeling) is useful for this. Relabeling is assumed to be an important building block for the construction of monotone classifiers and it is proved that it can improve the predictive performance. In this paper, we will address the construction of monotone datasets considering as noise the cases that do not meet the monotonicity restrictions. For the first time in the specialized literature, we propose the use of noise filtering algorithms in a preprocessing stage with a double goal: to increase both the monotonicity index of the models and the accuracy of the predictions for different monotonic classifiers. The experiments are performed over 12 datasets coming from classification and regression problems and show that our scheme improves the prediction capabilities of the monotonic classifiers instead of being applied to original and relabeled datasets. In addition, we have included the analysis of noise filtering process in the particular case of wine quality classification to understand its effect in the predictive models generated.},
	keywords = {Monotonic classification, Noise filtering, Ordinal classification, Preprocessing},
	issn = {0925-2312},
	doi = {https://doi.org/10.1016/j.neucom.2018.05.131},
	url = {http://www.sciencedirect.com/science/article/pii/S092523121930325X},
	author = {J. R. Cano and J. Luengo and S. Garc{\'\i}a}
}
@article {753,
	title = {KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining.},
	journal = {International Journal of Computational Intelligence Systems},
	volume = {10},
	number = {1},
	year = {2017},
	pages = {1238-1249},
	issn = {1875-6891},
	author = {I. Triguero and S. Gonzalez and J.M. Moyano and S. Garc{\'\i}a and J. Alcal{\'a}-Fdez and J. Luengo and A. Fern{\'a}ndez and M. J. del Jesus and L. S{\'a}nchez and F. Herrera and ATLANTIS PRESS.}
}
@article {262,
	title = {The Influence of Noise on the Evolutionary Fuzzy Systems for Subgroup Discovery},
	journal = {Soft Computing},
	volume = {20},
	year = {2016},
	note = {TIN2015-68454-R, TIN2014-57251-P, P11-TIC-7765, P12-TIC-2958},
	pages = {4313-4330},
	doi = {10.1007/s00500-016-2300-1},
	author = {J. Luengo and A.M. Garc{\'\i}a-Vico and M.D. P{\'e}rez-Godoy and C. J. Carmona}
}
@inbook {Cl15,
	title = {A first approach in the class noise filtering approaches for Fuzzy Subgroup Discovery},
	booktitle = {Advances in Intelligent Systems and Computing},
	volume = {368},
	year = {2015},
	note = {TIN2012-33856, TIN2011-28488, TIN2010-15055, P10-TIC-6858, P12-TIC-2958},
	pages = {387-399},
	publisher = {Springer},
	organization = {Springer},
	author = {C. J. Carmona and J. Luengo}
}
@conference {Clgd12b,
	title = {A preliminary study on missing data imputation in evolutionary fuzzy systems of subgroup discovery},
	booktitle = {IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	month = {June},
	pages = {1-7},
	address = {Brisbane (Australia)},
	author = {C. J. Carmona and J. Luengo and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {simidat225,
	title = {A Preliminary Study on Selecting the Optimal Cut Points in Discretization by Evolutionary Algorithms},
	booktitle = {1st International Conference on Pattern Recognition Applications and Methods (ICPRAM)},
	year = {2012},
	note = {TIN2011-28488,TIC-6858},
	month = {February},
	pages = {211-216},
	address = {Villamoura - (Portugal)},
	author = {S. Garc{\'\i}a and V. L{\'o}pez and J. Luengo and C. J. Carmona and F. Herrera}
}
@article {Clgd12,
	title = {An analysis on the use of pre-processing methods in evolutionary fuzzy systems for subgroup discovery},
	journal = {Expert Systems with Applications},
	volume = {39},
	year = {2012},
	note = {TIN-2008-06681-C06-02,TIC-3928},
	pages = {11404-11412},
	doi = {10.1016/j.eswa.2012.04.029},
	author = {C. J. Carmona and J. Luengo and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {simidat238,
	title = {Addressing Data Complexity for Imbalanced Data Sets: Analysis of SMOTE-based Oversampling and Evolutionary Undersampling},
	journal = {Soft Computing},
	volume = {15},
	number = {10},
	year = {2011},
	pages = {1909-1936},
	doi = {10.1007/s00500-010-0625-8},
	author = {J. Luengo and A. Fern{\'a}ndez and S. Garc{\'\i}a and F. Herrera}
}
@article {Dglch11,
	title = {Evolutionary Selection of Hyperrectangles in Nested Generalized Exemplar Learning},
	journal = {Applied Soft Computing},
	volume = {11},
	number = {3},
	year = {2011},
	note = {TIN2008-06681-C06-01},
	pages = {3032-3045},
	doi = {10.1016/j.asoc.2010.11.030},
	author = {S. Garc{\'\i}a and J. Derrac and J. Luengo and C. J. Carmona and F. Herrera}
}
@article {simidat237,
	title = {KEEL Data-Mining Software Tool: Data Set Repository, Integration of Algorithms and Experimental Analysis Framework},
	journal = {Journal of Multiple-Valued Logic and Soft Computing},
	volume = {17},
	number = {2-3},
	year = {2011},
	pages = {255-287},
	author = {J. Alcal{\'a}-Fdez and A. Fern{\'a}ndez and J. Luengo and J. Derrac and S. Garc{\'\i}a and L. S{\'a}nchez and F. Herrera}
}
@article {simidat187,
	title = {Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental Analysis of Power},
	journal = {Information Sciences},
	volume = {180},
	year = {2010},
	pages = {2044{\textendash}2064},
	doi = {10.1016/j.ins.2009.12.010},
	author = {S. Garc{\'\i}a and A. Fern{\'a}ndez and J. Luengo and F. Herrera}
}
@article {simidat239,
	title = {Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy and Comparative Study},
	journal = {IEEE Transactions on Evolutionary Computation},
	volume = {14},
	number = {6},
	year = {2010},
	pages = {913-941},
	doi = {10.1109/TEVC.2009.2039140},
	author = {A. Fern{\'a}ndez and J. Luengo and S. Garc{\'\i}a and E. Bernad{\'o}-Mansilla and F. Herrera}
}
@conference {simidat199,
	title = {Addressing Data-Complexity for Imbalanced Data-sets: A Preliminary Study on the Use of Preprocessing for C4.5},
	booktitle = {9th International Conference on Intelligent Systems Designs and Applications (ISDA)},
	year = {2009},
	pages = {523-528},
	author = {J. Luengo and A. Fern{\'a}ndez and F. Herrera and S. Garc{\'\i}a}
}
@conference {simidat82,
	title = {Estudio de la influencia de las medidas de complejidad de los datos en los Sistemas de Clasifcaci{\'o}n Basados en Reglas Difusas: An{\'a}lisis de la Raz{\'o}n Discriminante de Fisher},
	booktitle = {XIV Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2008},
	month = {September},
	pages = {257-263},
	address = {Mieres (Spain)},
	author = {J. Luengo and S. Garc{\'\i}a and J. R. Cano and F. Herrera}
}
