@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 {CANO2017128,
	title = {Prototype selection to improve monotonic nearest neighbor},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {60},
	year = {2017},
	pages = {128 - 135},
	abstract = {Student surveys occupy a central place in the evaluation of courses at teaching institutions. At the end of each course, students are requested to evaluate various aspects such as activities, methodology, coordination or resources used. In addition, a final qualification is given to summarize the quality of the course. The prediction of this final qualification can be accomplished by using monotonic classification techniques. The outcome offered by these surveys is particularly significant for faculty and teaching staff associated with the course. The monotonic nearest neighbor classifier is one of the most relevant algorithms in monotonic classification. However, it does suffer from two drawbacks, (a) inefficient execution time in classification and (b) sensitivity to no monotonic examples. Prototype selection is a data reduction process for classification based on nearest neighbor that can be used to alleviate these problems. This paper proposes a prototype selection algorithm called Monotonic Iterative Prototype Selection (MONIPS) algorithm. Our objective is two-fold. The first one is to introduce MONIPS as a method for obtaining monotonic solutions. MONIPS has proved to be competitive with classical prototype selection solutions adapted to monotonic domain. Besides, to further demonstrate the good performance of MONIPS in the context of a student survey about taught courses.},
	keywords = {Data reduction, Monotone nearest neighbor, Monotonic classification, Opinion surveys, Prototype selection},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2017.02.006},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197617300295},
	author = {J. R. Cano and Naif R. Aljohani and Rabeeh Ayaz Abbasi and Jalal S. Alowidbi and S. Garc{\'\i}a}
}
@article {CANO201794,
	title = {Training set selection for monotonic ordinal classification},
	journal = {Data \& Knowledge Engineering},
	volume = {112},
	year = {2017},
	pages = {94 - 105},
	abstract = {In recent years, monotonic ordinal classification has increased the focus of attention for machine learning community. Real life problems frequently have monotonicity constraints. Many of the monotonic classifiers require that the input data sets satisfy the monotonicity relationships between its samples. To address this, a conventional strategy consists of relabeling the input data to achieve complete monotonicity. As an alternative, we explore the use of preprocessing algorithms without modifying the class label of the input data. In this paper we propose the use of training set selection to choose the most effective instances which lead the monotonic classifiers to obtain more accurate and efficient models, fulfilling the monotonic constraints. To show the benefits of our proposed training set selection algorithm, called MonTSS, we carry out an experimentation over 30 data sets related to ordinal classification problems.},
	keywords = {Data preprocessing, machine learning, Monotonic classification, Ordinal classification, Training set selection},
	issn = {0169-023X},
	doi = {https://doi.org/10.1016/j.datak.2017.10.003},
	url = {http://www.sciencedirect.com/science/article/pii/S0169023X16303585},
	author = {J. R. Cano and S. Garc{\'\i}a}
}
