@article {796,
	title = {Smartdata: Data preprocessing to achieve smart data in R},
	journal = {Neurocomputing},
	volume = {360},
	year = {2019},
	note = {BigDaP-TOOLS - Ayudas Fundaci{\'o}n BBVA a Equipos de Investigaci{\'o}n Cient{\'\i}fica 2016},
	month = {09/2019},
	pages = {1-13},
	abstract = {As the amount of data available exponentially grows, data scientists are aware that finding the value in the data is key to a successful data exploiting. However, the data rarely presents itself in a ordered, clean way. In opposition to dealing with raw data, the term smart data is becoming more and more visible both in the specialized literature and companies. While software packages publicly exist to deal with raw data, there is no unified framework that encompasses all the required fields to transform such raw data to smart data. In this paper, the novel smartdata package is introduced. Written in R and available at CRAN repository, it includes the most recent and relevant algorithms to treat raw data from multiple perspectives, now unified under a simple yet powerful API, which enables the data scientist to easily pipeline their application. The main features of the package, as well as some illustrative examples of its use are detailed throughout this manuscript.},
	keywords = {Data preprocessing, machine learning, Preprocessing, Smart data},
	doi = {https://doi.org/10.1016/j.neucom.2019.06.006},
	author = {I. Cordon and Luengo, Juli{\'a}n and Garc{\'\i}a, Salvador and F. Herrera and Francisco Charte}
}
@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}
}
@conference {4295665,
	title = {Some Results about Mutual Information-based Feature Selection and Fuzzy Discretization of Vague Data},
	booktitle = {2007 IEEE International Fuzzy Systems Conference},
	year = {2007},
	month = {July},
	pages = {1-6},
	abstract = {Algorithms for preprocessing databases with incomplete and imprecise data are seldom studied, partly because we lack numerical tools to quantify the interdependency between fuzzy random variables. In particular, many filter-type feature selection algorithms rely on crisp discretizations for estimating the mutual information between continuous variables, effectively preventing the use of vague data. Fuzzy rule based systems pass continuous input variables, in turn, through their own fuzzification interface. In the context of feature selection, should we rank the relevance of the inputs by means of their mutual information, it might happen that an apparently informative variable is useless after having been codified as a fuzzy subset of our catalog of linguistic terms. In this paper we propose to address both problems by estimating the mutual information with the same set of fuzzy partitions that will be used to codify the antecedents of the fuzzy rules. That is to say, we introduce a numerical algorithm for estimating the mutual information between two fuzzified continuous variables. This algorithm can be included in certain feature selection algorithms, and can also be used to obtain the most informative fuzzy partition for the data. The use of our definition will be exemplified with the help of some benchmark problems.},
	keywords = {codification, computational linguistics, Data preprocessing, Feature extraction, feature selection, fuzzification interface, fuzzy discretization, fuzzy random variables, fuzzy rule based systems, fuzzy set theory, Fuzzy sets, Fuzzy systems, Information filtering, Information filters, knowledge based systems, linguistic terms, mutual information, Partitioning algorithms, Random variables, Spatial databases, vague data},
	issn = {1098-7584},
	doi = {10.1109/FUZZY.2007.4295665},
	author = {L. S{\'a}nchez and M. R. Suarez and J. R. Villar and I. Couso}
}
