@article {782, title = {A Comprehensive and Didactic Review on Multilabel Learning Software Tools}, journal = {IEEE Access}, volume = {8}, year = {2020}, note = {TIN2015-68854-R}, month = {03/2020}, pages = {50330-50354}, abstract = {Machine learning has become an everyday tool in so many fields that there is plenty of software to run many of these algorithms in every device, from supercomputers to embedded appliances. Most of these methods fall into the category known as standard learning, being supervised models (guided by pre-labeled examples) aimed to classify new patterns into exactly one category. This way, machine learning is in charge of getting rid of junk emails, labeling people in a picture, or detecting a fraudulent transaction when using a credit card. Aside from unsupervised learning methods, which are usually applied to group similar patterns, infer association rules and similar tasks, some non-standard supervised machine learning problems have been faced in late years. Among them, multilabel learning is arguably the most popular one. These algorithms aim to produce models in which each data pattern may be linked to several categories at once. Thus, a multilabel classifier generates a set of outputs instead of only one as a standard classifier does. However, software tools for multilabel learning tend to be scarce. This paper provides multilabel researchers with a comprehensive review of the currently available multilabel learning software. It is written following a didactic approach, focusing on how to accomplish each task rather than simply offering a list of programs and websites. The goal is to help finding the most appropriate resource to complete every step, from locating datasets and partitioning them to running many of the multilabel algorithms proposed in the literature until now.}, keywords = {machine learning, multilabel, non-standard learning, software, Tools}, doi = {https://doi.org/10.1109/ACCESS.2020.2979787}, author = {Francisco Charte} } @article {784, title = {Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications}, journal = {Neurocomputing}, volume = {410}, year = {2020}, note = {TIN2017-85827-P; RTI2018-098913-B-I00; PSI2015-65848-R; PGC2018-098813-B-C31; PGC2018-098813-B-C32; RTI2018-101114-B-I; TIN2017-90135-R; RTI2018-098743-B-I00; RTI2018-094645-B-I00; FPU15/06512; FPU17/04154; FJCI-2017{\textendash}33022; UMA18-FEDERJA-084; ED431C2017/12; ED431G/08; ED431C2018/29; Y2018/EMT-5062; ED431F2018/02; U01 AG024904; W81XWH-12-2-0012}, pages = {237-270}, abstract = {Artificial intelligence and all its supporting tools, e.g. machine and deep learning in computational intelligence-based systems, are rebuilding our society (economy, education, life-style, etc.) and promising a new era for the social welfare state. In this paper we summarize recent advances in data science and artificial intelligence within the interplay between natural and artificial computation. A review of recent works published in the latter field and the state the art are summarized in a comprehensive and self-contained way to provide a baseline framework for the international community in artificial intelligence. Moreover, this paper aims to provide a complete analysis and some relevant discussions of the current trends and insights within several theoretical and application fields covered in the essay, from theoretical models in artificial intelligence and machine learning to the most prospective applications in robotics, neuroscience, brain computer interfaces, medicine and society, in general.}, keywords = {AI for social well-being, Alzheimer, Artificial intelligence (AI), Artificial neural networks (ANNs), Autism, Big Data, Computational neuroethology, Deep learning, Dyslexia, Emotion recognition, evolutionary computation, Glaucoma, Human{\textendash}machine interaction, machine learning, Neuroscience, Ontologies, Parkinson, Reinforcement learning, Robotics, Virtual reality}, doi = {https://doi.org/10.1016/j.neucom.2020.05.078}, author = {Juan M.G{\'o}rriz and Javier Ram{\'\i}rez and Andr{\'e}s Ort{\'\i}z and Francisco J. Mart{\'\i}nez-Murcia and Ferm{\'\i}n Segovia and John Suckling and Matthew Leming and Yu-Dong Zhang and Jos{\'e} Ram{\'o}n {\'A}lvarez-S{\'a}nchez and Guido Bologna and Paula Bonomini and Fernando E. Casado and David Charte and Francisco Charte and Ricardo Contreras and Alfredo Cuesta Infante and Richard J. Duro and Antonio Fern{\'a}ndez Caballero and Eduardo Fern{\'a}ndez Jover and Pedro G{\'o}mez Vilda and Manuel Gra{\~n}a and F. Herrera and Roberto Iglesias and Anna Lekova and Javier de Lope and Ezequiel L{\'o}pez Rubio and Rafael Mart{\'\i}nez Tom{\'a}s and Miguel A. Molina-Cabello and Antonio S. Montemayor and Paulo Novais and Daniel Palacios-Alonso and Juan J. Pantrigo and Bryson R. Payne and F{\'e}lix de la Paz L{\'o}pez and Mar{\'\i}a Ang{\'e}lica Pinninghoff and Mariano Rinc{\'o}n and Jos{\'e} Santos and Karl Thurnhofer-Hemsi and Athanasios Tsanas and Ramiro Varela and Jose M. Ferr{\'a}ndez} } @article {777, title = {predtoolsTS: R package for streamlining time series forecasting}, journal = {Progress in Artificial Intelligence}, volume = {8}, year = {2019}, note = {TIN2015-68854-R}, month = {06/2019}, pages = {505{\textendash}510}, abstract = {Time series forecasting is a field of interest in many areas. Classically, statistical methods have been used to address this problem. In recent years, machine learning (ML) algorithms have been also applied with satisfactory results. However, ML software packages are not skilled to deal with raw sequences of temporal data, and therefore, it is necessary to transform these time series. This paper presents predtoolsTS, an R package that provides a uniform interface for applying both statistical and ML methods to time series forecasting. predtoolsTS comprises four modules: preprocessing, modeling, prediction and postprocessing, in order to deal with the whole process of time series forecasting.}, keywords = {machine learning, R, time series forecasting}, doi = {https://doi.org/10.1007/s13748-019-00193-z}, author = {Francisco Charte and Alberto Vico and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas} } @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} }