@conference {772,
	title = {A First Approximation to the Effects of Classical Time Series Preprocessing Methods on LSTM Accuracy},
	booktitle = {International Work-Conference on Artificial Neural Networks},
	year = {2019},
	note = {TIN2015-68454-R},
	month = {05/2019},
	pages = {270-280},
	abstract = {A convenient data preprocessing has proven to be crucial in order to achieve high levels of accuracy, time series being no exception. For this kind of forecasting tasks, several specialized preprocessing methods have been described, being trend analysis and seasonal analysis some of them. Several have been formally grouped around a methodology that is always applied to state of the art time series forecasting models, like the well known ARIMA.

LSTM is a relatively novel architecture which has been specifically designed to get rid of the vanishing gradient problem. In these models, great results have been seen when applied for time series forecasting. Still, little is known about the impact of these traditional preprocessing methods on the accuracy of LSTM.

In this work an empirical analysis on how classical time series preprocessing methods influence LSTM results is conducted. That all considered ones can potentially improve LSTM performance is concluded, being the seasonal component removal the filter that achieves better, most robust accuracy gain.},
	keywords = {LSTM, Preprocessing, time series},
	doi = {https://doi.org/10.1007/978-3-030-20521-8_23},
	author = {Daniel Trujillo Viedma and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@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 {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 {FERNANDEZ201397,
	title = {Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches},
	journal = {Knowledge-Based Systems},
	volume = {42},
	year = {2013},
	pages = {97 - 110},
	abstract = {The imbalanced class problem is related to the real-world application of classification in engineering. It is characterised by a very different distribution of examples among the classes. The condition of multiple imbalanced classes is more restrictive when the aim of the final system is to obtain the most accurate precision for each of the concepts of the problem. The goal of this work is to provide a thorough experimental analysis that will allow us to determine the behaviour of the different approaches proposed in the specialised literature. First, we will make use of binarization schemes, i.e., one versus one and one versus all, in order to apply the standard approaches to solving binary class imbalanced problems. Second, we will apply several ad hoc procedures which have been designed for the scenario of imbalanced data-sets with multiple classes. This experimental study will include several well-known algorithms from the literature such as decision trees, support vector machines and instance-based learning, with the intention of obtaining global conclusions from different classification paradigms. The extracted findings will be supported by a statistical comparative analysis using more than 20 data-sets from the KEEL repository.},
	keywords = {Cost-sensitive learning, Imbalanced data-sets, Multi-classification, Pairwise learning, Preprocessing},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2013.01.018},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705113000300},
	author = {Alberto Fernandez and Victoria L{\'o}pez and Mikel Galar and M. J. del Jesus and F. Herrera and ELSEVIER SCIENCE BV}
}
