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Publications (50)
This study explores the possibilities and challenges of artificial intelligence in higher education, specifically the role of Generative Artificial Intelligence in the classroom in order to achieve a more personalized and inclusive university education that enhances students' skills and where professors can devote more time to students thanks to th...
A methodology has been developed to quantify the economic value of the provisioning ecosystem services in the South-West of Europe (SUDOE) at any scale. This article describes the methodology that allows the quantification of nutritional and non-nutritional materials and their economic valuation related to agriculture activities at the regional sca...
Given the need for water use to be a crucial consideration in sustainable development, an adequate water allocation system across economic sectors is essential, especially in the face of increasing seasonal and perennial water scarcity. In an attempt to facilitate a socially and economically efficient adaptation to the climate emergency, we propose...
Background and objective
Tracking clean electricity generation in developing economies is highly challenging owing to the influence of turbulent external factors. Clean electricity is a significant enabler of striving toward environmental sustainability. In this research, we aim to model hydro, nuclear, and renewable electricity generation in India...
Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt fluctuations in financial markets. The...
The outbreak of the COVID-19 pandemic has transpired the global media to gallop with reports and news on the novel Coronavirus. The intensity of the news chatter on various aspects of the pandemic, in conjunction with the sentiment of the same, accounts for the uncertainty of investors linked to financial markets. In this research, Artificial Intel...
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. T...
Numerous studies have focused on variables related to preventing and promoting mental health in adolescence. The purpose of the present study was to analyze to what extent emotion regulation strategies mediate the effect of physical activity on adolescent mental health. The sample comprised 173 adolescents who completed the “Cognitive Emotion Regul...
The recent emergence and development of Metaverse platform have resulted in creating digital niche assets, which are argued to exert disruption in the global financial market. The dearth of dedicated research to demystify the dependence of Metaverse financial market on external factors is apparent. The current research endeavors to exemplify the dy...
The paper presents a framework to forecast futures prices of stocks listed on the National Stock Exchange (NSE) in India during normal (unaffected by the COVID-19 pandemic) and new normal times (affected by COVID-19 and a macroeconomic slowdown). The model leverages a structural model that determines the relevance of the explanatory features used i...
Identification of key determinants responsible for driving stock prices across the world is of paramount practical importance. The task is extremely arduous owing to sensitiveness of financial markets to macroeconomic shocks, external chaos, political instability and natural calamities. In this work, effort has been made to critically evaluate the...
Introduction:
The relevant scientific literature has confirmed the relationship between emotional intelligence (EI) and mental health. In addition, previous studies have underlined the importance of perceived EI between family members in the construction of one's own EI. Adolescence is considered to be a crucial stage in identity construction and...
Some of the most overlooked valuation systems in current literature are those based on expert algorithms. Yet these algorithms can form the basis of a good estimation of the value of real estate since they allow simple computational methods that use big data to be integrated with the appraiser’ own knowledge of the situation. The main usefulness of...
The close relationship between collateral value and bank stability has led to a considerable need to a rapid and economical appraisal of real estate. The greater availability of information related to housing stock has prompted to the use of so-called big data and machine learning in the estimation of property prices. Although this methodology has...
Statistical quality control procedures have become essential practices to ensure the competitiveness in any manufacturing process. Since the quality of manufactured goods usually depends on several correlated characteristics, statistical multivariate techniques are needed to detect and analyze out-of-control situations. The difficulties in the inte...
An essential guide to two burgeoning topics in machine learning – classification trees and ensemble learning
Ensemble Classification Methods with Applications in R introduces the concepts and principles of ensemble classifiers methods and includes a review of the most commonly used techniques. This important resource shows how ensemble classifica...
This chapter analyses some aspects related to the behavior and properties of individual classifiers. It focuses on some of the problems or difficulties derived from the use of these classifiers, such as lack of accuracy or instability. The chapter explores the generalization error of individual classifiers. This error will be disaggregated into the...
The R programming environment consists of a set of packages or libraries for data manipulation, calculation, and graphics. Among the libraries available in CRAN is adabag. The adabag package consists of a total of thirteen functions. The functions for each method are to build the boosting classifier and classify the samples in the training set, to...
This chapter deals with the study of combined classifiers, focusing mainly on the boosting method, but also collecting a classification of the ensemble methods and introducing the bagging and random forests methods. It presents some of the taxonomies of classifier combination methods. The chapter analyses the bagging approach, which is based on the...
Corporate failure prediction is traditionally an important management science task but became even more important during the last economic crisis. This chapter considers both the dichotomous and multiclass perspectives. Corporate failure prediction consists of separating the firms with a high probability of future failure from those that are consid...
To detect out-of control situations using multivariate control charts is relatively easy. However, the determination of the change causes is more difficult. In the last years the application of classification techniques to analyze the out-of-control signals has been proposed. These proposals include increasingly sophisticated methods but it is not...
Esta monografía, Combinación de árboles de clasificación para la Economía, pretende introducir al lector en los métodos de combinación de clasificadores mediante la exposición de las técnicas más utilizadas. El objetivo no es un análisis completo de las técnicas y su aplicación, ni un recorrido exhaustivo por todos los temas y aspectos que surgen d...
Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting) is the best known, although it is suitable only for dichotomous...
Boosting and bagging are two widely used ensemble methods for classification. Their common goal is to improve the accuracy of a classifier combining single classifiers which are slightly better than random guessing. Among the family of boosting algorithms, AdaBoost (adaptive boosting) is the best known, although it is suitable only for dichotomous...
We study the relationship between linear separability and the level of complexity of classification data sets. Linearly separable classification problems are generally easier to solve than non linearly separable ones. This suggests a strong correlation between linear separability and classification complexity. We propose a novel and simple method f...
This paper proposes a new method for constructing binary classification trees. The aim is to build simple trees, i.e. trees which are as less complex as possible, thereby facilitating interpretation and favouring the balance between optimization and generalization in the test data sets. The proposed method is based on the metaheuristic strategy kno...
En este trabajo se propone un nuevo método para la construcción de árboles binarios de clasificación. El objetivo es la construcción de árboles sencillos, es decir, con la menor complejidad posible, lo cual hace que sean de fácil interpretación y propicia el equilibrio entre optimización y generalización en los conjuntos test. El método propuesto s...
The most widely used tools in statistical quality control are control charts. However, the main problem of multivariate control charts, including Hotelling's T control chart, lies in that they indicate that a change in the process has happened, but do not show which variable or variables are the source of this shift. Although a number of methods ha...
Classification problems vary on their level of complexity. Several methods have been proposed to calculate this level but it remains difficult to measure. Linearly separable classification problems are amongst the easiest problems to solve. There is a strong correlation between the degree of linear separability of a problem and its level of complex...
In statistical quality control, one of the most widely used tools are the control
charts. The main problem of the multivariate control charts lies in that they only
indicate that a change in the process has happened, but they do not show which
variable or variables are the source of this change. In the specialized literature there
are many approach...
Abstract
1. Title: “Classification Trees to Interpret Out-Of-Control Signals in Multivariate Control Charts”
2. Objectives: In statistical quality control, one of the most widely used tools are the control charts. The main problem of the multivariate control charts, including the Hotelling‘s T2 control chart, lies in that they only indicate that...
The goal of this study is to show an alternative method to corporate failure prediction. In the last decades Artificial Neural Networks have been widely used for this task. These models have the advantage of being able to detect non-linear relationships and show a good performance in presence of noisy information, as it usually happens, in corporat...
Although property valuation models have become an important paradigm in real estate market research, the results of the most well-known approaches are limited due to various data-related problems such as the non-linearity of relationships, the presence of noise, or the absence of necessary information. This paper focuses on overcoming these obstacl...
Since the sixties, many classification techniques both from statistics and other scientific disciplines have been used to predict corporate failure. Multivariate linear discriminant analysis, however, continues to be one of the main reference methods principally because it is easy to apply and interpret. AdaBoost is a machine learning technique whi...
Since the sixties, many classification techniques both from statistics and other scientific disciplines have been used to predict corporate failure. Multivariate linear discriminant analysis, however, continues to be one of the main reference methods principally because it is easy to apply and interpret. AdaBoost is a machine learning technique whi...
Depends R (>= 2.4.0), rpart, mlbench Description This package implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, License GPL (>= 2) Description Fits the Adaboost.M1 algorithm proposed by Freund and Schapire in 1996 usi...
Predicting corporate failure is an important management science problem. This is a typical classification question where the
objective is to determine which indicators are involved in the failure/success of a corporation. Despite the importance of
this problem, until now only classical machine learning tools have been considered to tackle this clas...
Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure or success of a corporation. Despite the complexity of the matter, a two-class problem has usually been considered to tackle this classification task. The...
El objetivo de esta investigación consiste en la aplicación de modelos neuronales al estudio de las características socioeconómicas de las regiones europeas. Los modelos neuronales de Kohonen, también conocidos como Mapas de Rasgos Autoorganizados o SOFM1, constituyen un tipo de redes neuronales cuya principal característica es el uso de aprendizaj...
En este artículo se recogen algunos de los métodos de clasificación que más se utilizan, exponiendo brevemente en qué consiste cada uno de ellos y comparándolos en función de los resultados que obtienen. Entre los métodos paramétricos destaca el análisis discriminante que es la técnica estadística clásica para la clasificación. En él se establece u...
Because of the socioeconomic importance of the housing subsector in the local, regional, and national economy and its implications
for housing policy, this paper attempts to analyze the spatial behavior of the free housing price in the city of Albacete.
To achieve this aim, the authors have used the models and estimators imported from geology calle...
The concepts, principles and procedures for developing and implementing a credit-scoring model had been fully developed by the early 1970s. However, the acceptance of these methods by loan officers was not great because they usually applied some relatively subjective evaluation procedures using a heuristic rather than scientific criteria. Neverthel...
En este artículo se pretende plantear las ventajas que presenta la combinación de distintos clasificadores individuales para conseguir una mayor precisión de forma conjunta. Se estudian las razones fundamentales por las que se puede explicar la superioridad de estos métodos de agregación, que son básicamente tres, una razón estadística, una de comp...
