Estudio de métodos de selección de instancias

En la tesis se ha realizado un estudio de las técnicas de selección de instancias: analizando el estado del arte y desarrollando nuevos métodos para cubrir algunas áreas que no habían recibido la debida atención hasta el momento. Los dos primeros capítulos presentan nuevos métodos de selección de instancias para regresión, un tema poco estudiado hasta la fecha en la literatura. El tercer capítulo, estudia la posibilidad de cómo la combinación de algoritmos de selección de instancias para regresión ofrece mejores resultados que los métodos por sí mismos. El último de los capítulos presenta una novedosa idea: la utilización de las funciones hash localmente sensibles para diseñar dos nuevos algoritmos de selección de instancias para clasificación. La ventaja que presenta esta solución, es que ambos algoritmos tienen complejidad lineal. Los resultados de esta tesis han sido publicados en cuatro artículos en revistas JCR del primer cuartil.Ministerio de Economía, Industria y Competitividad, la Junta de Castilla y León y el Fondo Europeo para el Desarrollo Regional, proyectos TIN 2011-24046, TIN 2015-67534-P (MINECO/FEDER) y BU085P17 (JCyL/FEDER

Módulo VII.1. Intervención temprana y aplicación de recursos inteligentes: Internet of Things e Inteligencia Artificial

Este módulo ha planteado y presentado dos conceptos: Internet of things e Inteligencia Artificial, que permiten entender cómo funcionan y qué utilidades pueden tener en la atención temprana.“Specialized and updated training on supporting advance technologies for early childhood education and care professionals and graduates”, eEarlyCare-T, reference 2021-1-ES01-KA220-SCH-000032661, is co-financed by the European Union's Erasmus+ programme, line KA220 Strategic Partnerships Scholar associations

Evaluation of Functional Abilities in 0–6 Year Olds: An Analysis with the eEarlyCare Computer Application

The application of Industry 4.0 to the field of Health Sciences facilitates precise diagnosis and therapy determination. In particular, its effectiveness has been proven in the development of personalized therapeutic intervention programs. The objectives of this study were (1) to develop a computer application that allows the recording of the observational assessment of users aged 0–6 years old with impairment in functional areas and (2) to assess the effectiveness of computer application. We worked with a sample of 22 users with different degrees of cognitive disability at ages 0–6. The eEarlyCare computer application was developed with the aim of allowing the recording of the results of an evaluation of functional abilities and the interpretation of the results by a comparison with "normal development". In addition, the Machine Learning techniques of supervised and unsupervised learning were applied. The most relevant functional areas were predicted. Furthermore, three clusters of functional development were found. These did not always correspond to the disability degree. These data were visualized with distance map techniques. The use of computer applications together with Machine Learning techniques was shown to facilitate accurate diagnosis and therapeutic intervention. Future studies will address research in other user cohorts and expand the functionality of their application to personalized therapeutic programs.Vice-rectorate for Research and Knowledge Transfer of the University of Burgos for making the development of the software possible through the VI Edition of the Call for Proofs of Concept: Impulse for the valorization and marketing of research results (2018–2019) and VII Edition of the Call for Proofs of Concept: Impulse for the valorization and marketing of research results (2019–2020)

Module IV.1. Observation and Evaluation Techniques from Intelligent Resources: Introduction to Data Mining

En este tema IV.1 se han abordado conceptos básicos relacionados con la Minería de Datos, así como algunas técnicas sencillas de Minería de datos para aplicar a investigaciones en el campo de la atención temprana.“Specialized and updated training on supporting advance technologies for early childhood education and care professionals and graduates”, eEarlyCare-T, reference 2021-1-ES01-KA220-SCH-000032661, is co-financed by the European Union's Erasmus+ programme, line KA220 Strategic Partnerships Scholar associations

Evolutionary prototype selection for multi-output regression

A novel approach to prototype selection for multi-output regression data sets is presented. A multi-objective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction quality expressed in terms of root mean squared error. A multi-target regressor based on k-NN was used for that purpose during the training to evaluate the error, while the tests were performed using four different multi-target predictive models. The distance matrices used by the multi-target regressor were cached to accelerate operational performance. Multiple Pareto fronts were also used to prevent overfitting and to obtain a broader range of solutions, by using different probabilities in the initialization of populations and different evolutionary parameters in each one. The results obtained with the benchmark data sets showed that the proposed method greatly reduced data set size and, at the same time, improved the predictive capabilities of the multi-output regressors trained on the reduced data set.NCN (Polish National Science Center) grant “Evolutionary Methods in Data Selection” No. 2017/01/X/ST6/00202, project TIN2015-67534-P (MINECO/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, and project BU085P17 (JCyL/FEDER, UE) of the Junta de Castilla y León cofinanced with European Union FEDER funds

Effectiveness of Blended Learning in Nursing Education

Currently, teaching in higher education is being heavily developed by learning management systems that record the learning behaviour of both students and teachers. The use of learning management systems that include project-based learning and hypermedia resources increases safer learning, and it is proven to be effective in degrees such as nursing. In this study, we worked with 120 students in the third year of nursing degree. Two types of blended learning were applied (more interaction in learning management systems with hypermedia resources vs. none). Supervised learning techniques were applied: linear regression and k-means clustering. The results indicated that the type of blended learning in use predicted 40.4% of student learning outcomes. It also predicted 71.9% of the effective learning behaviors of students in learning management systems. It therefore appears that blended learning applied in Learning Management System (LMS) with hypermedia resources favors greater achievement of effective learning. Likewise, with this type of Blended Learning (BL) a larger number of students were found to belong to the intermediate cluster, suggesting that this environment strengthens better results in a larger number of students. BL with hypermedia resources and project-based learning increase students´ learning outcomes and interaction in learning management systems. Future research will be aimed at verifying these results in other nursing degree courses.Consejería de Educación de la Junta de Castilla y León (Spain) (Department of Education of the Junta de Castilla y León), Grant number BU032G19, and grants from the University of Burgos for the dissemination and the improvement of teaching innovation experiences of the Vice-Rectorate of Teaching and Research Staff, the Vice-Rectorate for Research and Knowledge Transfer, 2020, at the University of Burgos (Spain)

When is resampling beneficial for feature selection with imbalanced wide data?

This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests. Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.“La Caixa” Foundation, under agreement LCF/PR/PR18/51130007. This work was also supported by the Junta de Castilla León under project BU055P20 (JCyL/FEDER, UE) and by the Ministry of Science and Innovation under project PID2020-119894GB-I00, co-financed through European Union FEDER funds

Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark

One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an acceptable execution time. In this paper we present Approx-SMOTE, a parallel implementation of the SMOTE algorithm for the Apache Spark framework. The key difference with the original SMOTE, besides parallelism, is that it uses an approximated version of k-Nearest Neighbor which makes it highly scalable. Although an implementation of SMOTE for Big Data already exists (SMOTE-BD), it uses an exact Nearest Neighbor search, which does not make it entirely scalable. Approx-SMOTE on the other hand is able to achieve up to 30 times faster run times without sacrificing the improved classification performance offered by the original SMOTE.“La Caixa” Foundation, under agreement LCF/PR/PR18/51130007. This work was supported by the Junta de Castilla y León under project BU055P20 and by the Ministry of Science and Innovation of Spain under project PID2020-119894 GB-I00, co-financed through European Union FEDER funds. It also was supported through Consejería de Educación of the Junta de Castilla y León and the European Social Fund through a pre-doctoral grant (EDU/1100/2017). This material is based upon work supported by Google Cloud