Jose-Francisco Díez-Pastor

Jose-Francisco Díez-Pastor
Universidad de Burgos | UBU · Civil Engineering

About

38
Publications
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Introduction
Skills and Expertise

Publications

Publications (38)
Article
Full-text available
Learning management systems (LMSs) that incorporate hypermedia Smart Tutoring Systems and personalized student feedback can increase self-regulated learning (SRL), motivation, and effective learning. These systems are studied with the following aims: (1) to verify whether the use of LMS with hypermedia Smart Tutoring Systems improves student learni...
Article
Purpose – Recent research in higher education has pointed out that personalized e-learning through the use of learning management systems, such as Moodle, improves the academic results of students and facilitates the detection of at-risk students. Design/methodology/approach – A sample of 124 students following the Degree in Health Sciences at the...
Article
Full-text available
Purpose Recent research in higher education has pointed out that personalized e-learning through the use of learning management systems, such as Moodle, improves the academic results of students and facilitates the detection of at-risk students. Design/methodology/approach A sample of 124 students following the Degree in Health Sciences at the Uni...
Article
Full-text available
In this paper, the focus is on the application of prototype selection to multi-label data sets as a preliminary stage in the learning process. There are two general strategies when designing Machine Learning algorithms that are capable of dealing with multi-label problems: data transformation and method adaptation. These strategies have been succes...
Article
Full-text available
Variscite is an aluminium phosphate mineral widely used as a gemstone in antiquity. Knowledge of the ancient trade in variscite has important implications on the historical appreciation of the commercial and migratory movements of human population. The mining complex of Gavà, which dates from the Neolithic, is one of the oldest underground mine sit...
Article
The theoretical background to automata and formal languages represents a complex learning area for students. Computer tools for interacting with the algorithm and interfaces to visualize its different steps can assist the learning process and make it more attractive. In this paper, we present a web application for learning some of the most common a...
Article
Full-text available
A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by inst...
Article
Full-text available
The multi-label classification problem is an extension of traditional (single-label) classification, in which the output is a vector of values rather than a single categorical value. The multi-label problem is therefore a very different and much more challenging one than the single-label problem. Recently, multi-label classification has attracted i...
Article
The use of biosolids for soil improvement and for the reduction of inorganic fertilization costs has been a common practice in recent decades and is being used more and more often as inorganic fertilization cost increases. This practice is useful because it can be effective for the recovery of low fertility soils and to recycle urban and industrial...
Article
Full-text available
Learning Management System (LMS) platforms provide a wealth of information on the learning patterns of students. Learning Analytics (LA) techniques permit the analysis of the logs or records of the activities of both students and teachers on the on-line platform. The learning patterns differ depending on the type of Blended Learning (B-Learning). I...
Article
Full-text available
Instance selection is a popular preprocessing task in knowledge discovery and data mining. Its purpose is to reduce the size of data sets maintaining their predictive capabilities. The usual emerging problem at this point is that these methods quite often suffer of high computational complexity, which becomes highly inconvenient for processing huge...
Article
Full-text available
Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems...
Article
Machine Learning has two central processes of interest that captivate the scientific community: classification and regression. Although instance selection for classification has shown its usefulness and has been researched in depth, instance selection for regression has not followed the same path and there are few published algorithms on the subjec...
Article
Ensembles are learning methods the operation of which relies on a combination of different base models. The diversity of ensembles is a fundamental aspect that conditions their operation. Random Feature Weights (\({\mathcal {RFW}}\)) was proposed as a classification-tree ensemble construction method in which diversity is introduced into each tree b...
Article
An important step in building expert and intelligent systems is to obtain the knowledge that they will use. This knowledge can be obtained from experts or, nowadays more often, from machine learning processes applied to large volumes of data. However, for some of these learning processes, if the volume of data is large, the knowledge extraction pha...
Conference Paper
Binarization techniques deal with multiclass classification problem combining several binary classifiers. They were originally introduced for dealing with multiclass problems with methods that were only able to deal with two classes (e.g., SVM). Nevertheless, it has been shown that they can also be useful with classification methods able to deal di...
Article
Two new methods for tree ensemble construction are presented: G-Forest and GAR-Forest. In a similar way to Random Forest, the tree construction process entails a degree of randomness. The same strategy used in the GRASP metaheuristic for generating random and adaptive solutions is used at each node of the trees. The source of diversity of the ensem...
Article
Full-text available
Bachelor and master's qualifications include assignments that involve the preparation of final projects. Their underlying pedagogical model is often based on final or end-of-course projects, which carry a high number of ECTS credits (12 or more), to be completed over one semester. Each project, which simulates a real life professional situation, is...
Conference Paper
This paper describes the process of detection of defects in metallic pieces through the analysis of X-ray images. The images used in this work are highly variable (several different pieces, different views, variability introduced by the inspection process such as positioning the piece). Because of this variability, the sliding window technique has...
Article
Rotation Forest, originally proposed for the combination of classifiers, has shown itself to be very competitive, when compared with other ensemble construction methods. In this paper, the performance of Rotation Forest for combining regressors is investigated using a broad range of datasets, 61 in total, which vary in size from 13 to more than 40,...
Conference Paper
Full-text available
In the Random Oracle ensemble method, each base classifier is a mini-ensemble of two classifiers and a randomly generated oracle that selects one of the two classifiers. The performance of this method have been previously studied, but not for imbalanced data sets. This work studies its performance for this kind of data. As the Random Oracle ensembl...
Conference Paper
Disturbing Neighbors (DN) is a method for generating classifier ensembles. Moreover, it can be combined with any other ensemble method, generally improving the results. This paper considers the application of these ensembles to imbalanced data: classification problems where the class proportions are significantly different. DN ensembles are compare...
Article
Industrial solutions for surface roughness prediction are in great demand, especially in high-torque milling operations, owing to the exponential expansion of wind power energy generation over the past decade. In this paper, we use Boosting Projections to predict surface roughness in high-torque, high-power face milling operations. A data set is ge...
Conference Paper
Two contexts may be considered, in which it is of interest to reduce the dimension of a data set. One of these arises when the intention is to mitigate the curse of dimensionality, when the data set will be used for training a data mining algorithm with a heavy computational load. The other is when one wishes to identify the data set attributes tha...
Article
Surface roughness plays a key role in the performance of machined components—specially dies and moulds—manufactured for the aerospace and automotive industries, among others. However, roughness can only be measured off-line after the part has been machined, when cutting conditions may no longer be adjusted to surface roughness requirements. A relia...
Conference Paper
Ensembles of decision trees are considered for imbalanced datasets. Conventional decision trees (C4.5) and trees for imbalanced data (CCPDT: Class Confidence Proportion Decision Tree) are used as base classifiers. Ensemble methods, based on undersampling and oversampling, for imbalanced data are considered. Conventional ensemble methods, not specif...
Conference Paper
Full-text available
This paper proposes a method for constructing ensembles of decision trees: GRASP Forest. This method uses the metaheuristic GRASP, usually used in optimization problems, to increase the diversity of the ensemble. While Random Forest increases the diversity by randomly choosing a subset of attributes in each tree node, GRASP Forest takes into accoun...
Conference Paper
Model trees are decision trees with linear regression functions at the leaves. Although originally proposed for regression, they have also been applied successfully in classification problems. This paper studies their performance for imbalanced problems. These trees give better results that standard decision trees (J48, based on C4.5) and decision...
Chapter
Full-text available
This paper considers the use of Random Oracles in Ensembles for regression tasks. A Random Oracle model (Kuncheva and Rodríguez, 2007) consists of a pair of models and a fixed randomly created "oracle" (in the case of the Linear Random Oracle, it is a hyperplane that divides the dataset in two during training and, once the ensemble is trained, deci...
Conference Paper
Functional Trees are one type of multivariate trees. This work studies the performance of different ensemble methods (Bagging, Random Subspaces, AdaBoost, Rotation Forest) using three variants (multivariate internal nodes, multivariate leaves or both) of these trees as base classifiers. The best results, for all the ensemble methods, are obtained u...
Conference Paper
Full-text available
We describe an artificial vision system used to recognize the Spanish car license plate numbers in raster images. The algorithm is designed to be independent of the distance from the car to the camera, the size of the plate number, the inclination and the light conditions. In the preprocessing steps, the algorithm takes a raster image as input and...

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Projects (2)
Project
Encontrar un modelo predictivo que detecte situaciones de crisis epilepticas durante el sueño
Project
The development of a Virtual Reality Simulator that can help factory workers to learn the main human risks during the control of bridge cranes. Artificial Intelligence techniques will be used to analyse the madurity of the workers in such tasks. Oculus Rift and Unreal Engine will be used to create the VR-Serious game.