@article {787,
	title = {Similarity-based and Iterative Label Noise Filters for Monotonic Classification},
	journal = {Proceedings of the 53rd Hawaii International Conference on System Sciences},
	year = {2020},
	note = {TIN2017-89517-P; TEC2015-69496-R; BigDaP-TOOLS - Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica 2016},
	pages = {1698-1706},
	abstract = {Monotonic ordinal classification has received an increasing interest in the latest years. Building monotone models from these problems usually requires datasets that verify monotonic relationships among the samples. When the monotonic relationships are not met, changing the labels may be a viable option, but the risk is high: wrong label changes would completely change the information contained in the data. In this work, we tackle the construction of monotone datasets by removing the wrong or noisy examples that violate monotonicity restrictions. We propose two monotonic noise filtering algorithms to preprocess the ordinal datasets and improve the monotonic relations between instances. The experiments are carried out over eleven ordinal datasets, showing that the application of the proposed filters improve the prediction capabilities over different levels of noise.},
	keywords = {Monotonic classification, noise, noise filter, Ordinal classification, Soft Computing: Theory Innovations and Problem Solving Benefits},
	doi = {https://doi.org/10.24251/HICSS.2020.210},
	author = {J. R. Cano and Luengo, Juli{\'a}n and Garc{\'\i}a, Salvador}
}
@article {785,
	title = {Monotonic classification: An overview on algorithms, performance measures and data sets},
	journal = {Neurocomputing},
	volume = {341},
	year = {2019},
	note = {TIN2017-89517-P; TIN2015-70308-REDT; TIN2014-54583-C2-1-R; TEC2015-69496-R},
	month = {05/2019},
	pages = {168-182},
	abstract = {Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.},
	keywords = {Monotonic classification, Monotonic data sets, Ordinal classification, Software Performance metrics, Taxonomy},
	doi = {https://doi.org/10.1016/j.neucom.2019.02.024},
	author = {J. R. Cano and Pedro Antonio Guti{\'e}rrez and Bartosz Krawczyk and Michat Wo{\'z}niak and Garc{\'\i}a, Salvador}
}
