CO2 emissions play a crucial role in international politics. Countries enter into agreements to reduce the amount of ...

Sarcomas are rare mesodermal tumors of heterogeneous nature and have a higher incidence in children. The relative 5-year ...

Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care ...

Solar energy is one of the most common and promising sources of renewable energy. In photovoltaic (PV) systems, operators ...

Esta tesis, presentada como un compendio de artículos de investigación, aborda la predicción de series temporales en un ...

Autonomous driving is one of the most important technological challenges of this century. Its development will revolutionize ...

Most of the current data sources generate large amounts of data over time. Renewable energy generation is one example of ...

Autonomous vehicles are equipped with complimentary sensors to perceive the environment accurately. Deep learning models ...

Background and Objectives: The burst of high-throughput omics technologies has given rise to a new era in systems biology, ...

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. ...

Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly ...

In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural ...

Object detection has been one of the most active topics in computer vision for the past years. Recent works have mainly ...

The morphology of nanoparticles governs their properties for a range of important applica tions. Thus, the ability to ...

Background: A clinical decision support system (CDSS) has been designed to predict the outcome (overall survival) by ...

Generally, classification problems catalog instances according to their target variable with out considering the relation ...

This paper presents a workbench to get simple neural classifcation models based on product evolutionary networks via a ...

Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer. Although the ...

Data streaming classification has become an essential task in many fields where real-time decisions have to be made based ...

This study proposes a novel bioinspired metaheuristic simulating how the coronavirus spreads and infects healthy people. ...

A new transfer learning strategy is proposed for image classification in this work, based on an 8-layer convolutional ...

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate ...

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric ...

This paper explores widely the data preparation stage within the process of knowledge discovery and data mining via feature ...

This paper presents an evolutionary method for modifying the behaviour of the k-Nearest-Neighbour clas sifier (kNN) called ...

Analyzing land use and land cover (LULC) using remote sensing (RS) imagery is essential for many environmental and social ...

The use of electric vehicle across the world has become one of the most challenging issues for environmental policies. The ...

Clustering is one of the most commonly used techniques in data mining. Its main goal is to group objects into clusters so ...

Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mining. Most clustering ...

Unemployment in Spain is one of the biggest concerns of its inhabitants. Its unemployment rate is the second highest in ...

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to new protection ...

This editorial summarizes the performance of the special issue entitled Data Science and Big Data in Energy Forecasting, ...

El modelo de negocio tradicional de las compañías energéticas está cambiando los últimos años. La introducción de los ...

New technologies such as sensor networks have been incorporated into the management of buildings for organizations and ...

Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. ...

The continuous evaluation allows for the assessment of the progressive assimilation of concepts and the competences that ...

Clustering analysis is one of the most used Machine Learning techniques to discover groups among data objects. Some ...

The main objective of this paper is the application of big data analytics to a real case in the field of smart electric ...

En este vídeo divulgativo se hace una introducción a una de las técnicas de análisis de datos más usada, el clustering. En ...

El clustering es una de las técnicas más utilizadas en minería de datos. Tiene como objetivo principal agrupar datos en ...

El cliente doméstico era el gran olvidado del sistema eléctrico. A pesar de su peso en el consumo total, hasta hace pocos ...

This paper addresses the situation that may happen after the application of feature subset selection in terms of a reduced ...

Breast cancer is the most common cause of cancer death in women. Today, post-transcriptional protein products of the genes ...

Alzheimer’s disease is a complex progressive neurodegenerative brain disorder, being its prevalence ex pected to rise over ...

La dificultad de aplicar técnicas de análisis de datos al problema de la calidad del software radica principalmente en dos ...

This editorial summarizes the performance of the special issue entitled Energy Time Series Forecasting, which was published ...

There exist several works in the literature in which fitness functions based on a combination of weighted measures for the ...

Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information about forest structure. ...

This paper presents a quality enhancement of the selected features by a hybrid filter-based jointly on feature ranking and ...

A framework that combines feature selection with evolution ary artificial neural networks is presented. This paper copes ...

This work presents an evolutionary approach to modify the voting system of the k-nearest neighbours (kNN) rule we called ...

K-Means and Bisecting K-Means clustering algorithms need the optimal number into which the dataset may be divided. Spark ...

El término big data se ha extendido rápidamente en el área de la minera de datos debido a que las grandes cantidades de ...

This paper presents a novel procedure to apply in a sequential way two data preparation techniques from a different nature ...

Association rule mining is a well-known methodology to discover significant and apparently hidden relations among attributes ...

In this paper a kernel for time-series data is introduced so that it can be used for any data mining task that relies on ...

En este trabajo se propone el uso de conocimiento a priori como heurística en métodos de inferencia de redes de genes a ...

This paper evaluates the performance of different types of Regression Trees (RTs) in a real problem of very short-term ...

Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques ...

Light Detection and Ranging (LiDAR) is a remote sensor able to extract three-dimensional information. Environmental models ...

This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association ...

Data classification is a critical step to convert remotely sensed data into thematic information. Environmental researchers have ...

The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate ...

This work presents an evolutionary approach to modify the voting system of the k-Nearest Neighbours (kNN). The main novelty ...

Light detection and ranging (LiDAR) has become an important tool in forestry. LiDAR-derived models are mostly developed ...

This paper introduces two statistical outlier detection approaches by classes. Experiments on binary and multi-class ...

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the ...

This paper introduces the use of an ant colony optimization (ACO) algorithm, called Ant System, as a search method in two ...

Light Detection and Ranging (LiDAR) is a remote sensor able to extract vertical information from sensed objects. ...

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering techniques ...

Digital forensics research includes several stages. Once we have collected the data the last goal is to obtain a model in ...

Imbalanced data is a common problem in data mining when dealing with classi cation problems, where samples of a class vastly ...

This paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural networks. ...

En la literatura, existen numerosas técnicas para mejorar el rendimiento de la regla de los k vecinos más cercanos (en ...

In this paper a kernel for time-series data is presented. The main idea of the kernel is that it is designed to recognize ...

Context: Although many papers have been published on software defect prediction techniques, machine learning approaches ...

There exist several fitness function proposals based on a combination of weighted objectives to optimize the discovery of ...

Esta propuesta de tesis está estrechamente relacionada con el campo de las técnicas inteligentes y el soft computing. A ...

Land use and land covers (LULC) maps are remote sensing products that are used to classify areas into different landscapes. ...

Data mining methods in software engineering are becoming increasingly important as they can support several aspects of the ...

Este artículo presenta los cambios experimentados en las asignaturas relacionadas con la introducción a la programación, ...

Light Detection and Ranging (LIDAR) has become a very important tool to many environmental applications. This work proposes to ...

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern ...

In this work, we describe a classification system to automatically recognize the pattern of HEp-2 cells within IIF images. ...

Different approaches of feature weighting and k-value selection to improve the nearest neighbour technique can be found ...

Actualmente, la aplicación de técnicas estadísticas es una necesidad cuando se trabaja con metaheurísticas. A pesar de que ...

We address the feature subset selection problem for classification tasks. We examine the performance of two hybrid strategies ...

La simulación se ha utilizado con frecuencia en los últimos años como herramienta de ayuda a la optimización de la toma ...

La aplicación de métodos de Minería de Datos a la Ingeniería del Software tiene una importancia creciente en distintos ...

This paper introduces a methodology that improves the accuracy of a two-stage algorithm in evolutionary product unit neural ...

Many approaches are currently devoted to find DNA motifs in nucleotide sequences. However, this task remains challenging ...

In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quan titative Association ...

Airborne LiDAR (Light Detection and Ranging) has become an excellent tool for accurately assessing vegetation characteristics ...

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The ...

This paper presents a new approach to forecast the behavior of time series based on similarity of pattern sequences. ...

The microarray technique is able to monitor the change in concentration of RNA in thousands of genes simultaneously. The ...

The analysis of microarray data is a computational challenge due to the characteristics of these data. Clustering techniques ...

Human impact on the natural environment is an evident global fact. Natural, industrial and touristic areas coexist in a ...

Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages ...

This paper presents the analysis of relationships among different interestingness measures of quality of association rules ...

An evolutionary approach for finding existing relationships among several variables of a multidimensional time series is ...

The forecasting process of real-world time series has to deal with especially unexpected values, commonly known as outliers. ...

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering ...

Nowadays, much effort is being devoted to develop techniques that forecast natural disasters in order to take ...

Analyzing microarray data represents a computational challenge due to the characteristics of these data. Clustering ...

The aim of this workshop is to provide a forum for discussion and debate on the application of knowledge representation ...

This paper proposes a Radial Basis Function Neural Network (RBFNN) which reproduces different Radial Basis Functions (RBFs) ...

Nearest neighbour (NN) is a very common classifier used to develop important remote sensing products like land use and ...

Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. ...

Background: Novel strategies are required in order to handle the huge amount of data produced by microarray technologies. ...

This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, ...

Remote sensing based on imagery has traditionally been the main tool used to extract land uses and land cover (LULC) maps. ...

The characterization of defective modules in software engineering remains a challenge. In this work, we use data mining ...

The characterisation of defective modules in software engineering remains a challenge. In this work, we use data mining ...

This work presents the discovering of association rules based on evolutionary techniques in order to obtain relationships ...

The area of Huelva, in the South of Spain, is a well-known case of human pressure on the natural environment. In Huelva, ...

This work aims to improve an existing time series forecasting algorithm –LBF– by the application of frequent episodes ...

Las técnicas de minería de datos están encaminadas a desarrollar algoritmos que sean capaces de tratar y analizar datos ...

Este trabajo presenta el descubrimiento de reglas de asociación basadas en técnicas evolutivas para obtener relaciones ...

Many software engineering datasets are highly unbalanced, i.e., the number of instances of a one class outnumber the number ...

The enormous increase of the size in databases makes finding an optimal subset of features extremely difficult. In this ...

LIDAR data acquisition is becoming an indispensable task for terrain characterization in large surfaces. In Mediterranean ...

A new approach is presented in this work with the aim of predicting time series behaviors. A previous labeling of the ...

This paper presents an evolutionary technique applied to the optimal short-term scheduling (24 h) of the electric ...

La presente edición de IEEE América Latina corresponde con una selección de los mejores trabajos presentados en la undécima ...

In this article, a set of clustering algorithms based on Fuzzy Logic and Data Mining are applied, allowing to obtain data ...

Clustering is used to generate groupings of data from a large dataset, with the intention of representing the behavior of ...

Breast cancer is one of the diseases causing the largest number of deaths among women. Its early detection has been proved ...

This paper presents a simple technique to forecast next-day electricity market prices based on the weighted nearest neighbors ...

En este capítulo se presenta un nuevo método para la aplicación de herramientas de Minería de Datos sobre la etapa de ...

La principal tarea de las técnicas de clustering es formar grupos de elementos que presenten una conducta similar a partir ...

En este capítulo se presentan algunas contribuciones en el campo de la toma de decisiones en Proyectos de Desarrollo de ...

One of the problems found in generic project databases, where the data is collected from different organizations, is the ...

Some of the most influential factors in the quality of the solutions found by an evolutionary algorithm (EA) are a ...

Clustering is a process of grouping similar elements gathered or occurred closely together. This paper presents two ...

Esta tesis doctoral se enmarca en el campo del aprendizaje automático y aborda uno de sus principales problemas, como es ...

La Minería de Datos ha experimentado en los últimos años una notable explosión de interés tanto en ámbitos académicos ...

Hoy en día, la minería de datos (MD) está consiguiendo cada vez más captar la atención de las empresas. Todavía es infrecuente ...

Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper ...

This paper presents an empirical study analysing the relationship between a set of metrics for Fourth–Generation Languages ...

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels ...

Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper ...

A tool to obtain a classifier system from labelled databases is presented. The result is a hierarchical set of rules to ...

Different ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several ...

The increasing amount of information available is encouraging the search for efficient techniques to improve the data ...

La selección de atributos es una técnica de preprocesamiento que extrae atributos relevantes del conjunto total de atributos, ...

In this work, we suggest a new feature selection technique that lets us use the wrapper approach for finding a well suited ...

Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper ...

Visualization has become an essential support throughout the KDD process in order to extract hidden information from huge ...

This paper describes an approach based on evolutionary algorithms, HIDER ( erarchical cision ules), for learning rules in ...

En este artículo presentamos FACIL, un algoritmo de aprendizaje incremental dirigido a la clasificación de data streams ...

Esta tesis está enmarcada básicamente dentro de dos campos de investigación, la minería de datos y la optimización. ...

This paper presents a comprehensive study of gene expression patterns originating from a diffuse large B–cell lymphoma ...

By means of an acid plant it is possible to transform sulphuric dioxide (SO2) into sulphuric acid, using for this a set ...

One of the main challenges that the project managers have during the building process of a software development project ...

Segmentation algorithms emerge observing fluctuations of DNA sequences in alternative homogeneous domains, which are named ...

Los algoritmos evolutivos conforman una de las más importantes familias de modelos computacionales con aplicación en el ...

In this paper we present a new method for the application of data mining tools on the management phase of software development ...

Visualization techniques provide an outstanding role in KDD process for data analysis and mining. However, one image does ...

This paper presents a scalable learning algorithm to classify numerical, low dimensionality, high-cardinality, time-changing ...

Segmentation algorithms differ from clustering algorithms with regard to how to deal with the physical location of genes ...

We propose a new feature selection criterion not based on calculated measures between attributes, or complex and costly ...

Progress in digital data acquisition and storage technology has resulted in the growth of huge databases. Nevertheless, ...

This coordinated project develops, studies and researches into several key activities for software engineering project ...

The application of data mining techniques to the managing of software development projects (SDP) is not an uncommon ...

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant ...

To select an adequate coding is one of the main problems in applications based on Evolutionary Algorithms. Many codings ...

The increasing amount of information available is encouraging the search for efficient techniques to improve the data ...

As an analysis of the classification accuracy bound for the Nearest Neighbor technique, in this work we have studied if ...

E–mail is one of the most common ways to communicate, assuming, in some cases, up to 75% of a company’s communication, in ...

This paper presents an incremental and scalable learning algorithm in order to mine numeric, low dimensionality, ...

This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to ...

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant ...

In this paper, an evolutionary technique applied to the optimal short-term scheduling (24 hours) of the electric energy ...

In this paper, we present a strategy to induce knowledge as support decision making in Software Development Projects (SDP). ...

In this paper, we propose a new feature selection criterion. It is based on the projections of data set elements onto each ...

This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation ...

Great organizations collect open-ended and time-changing data received at a high speed. The possibility of extracting ...

This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules ...

This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous ...

This paper presents a new approach to 2nding representative patterns for dataset editing. The algorithm patterns by ...

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant ...

En este artículo presentamos un nuevo método, de nominado OBLIC, para inducción de reglas de clasificación oblicuas no ...

Association rules are one of the most used tools to discover relationships among attributes in a database. Nowadays, there ...

Decision making has been traditionally based on a managers experience. This paper, however, discusses how a software ...

The attribute selection techniques for supervised learning, used in the preprocessing phase to emphasize the most relevant ...

Association rules are one of the most used tools to discover relationships among attributes in a database. Nowadays, there ...

The most influential factors in the quality of the solutions found by an evolutionary algorithm are a correct coding of ...

In this paper, we offer a new method to induce interesting knowledge from the relevant sets of data in databases for ...

In the framework of competitive markets, the market’s participants need energy price forecasts in order to determine their ...

Software Project Simulator and Evolutionary Computation are combined to generate decision rules. The purpose is to provide ...

In today’s deregulated markets, forecasting energy prices is becoming more and more important. In the short term, expected ...

In this paper, we present a new algorithm based on the nearest neighbours method, for discovering groups and identifying ...

Qualitative reasoning methods have proven useful to extract qualitative information from quantitative data in a wide ...

We present a new classification system based on Evolutionary Algorithm (EA), OBLIC. This tool is an OBLIque Classification ...

The Simulators of Software Development Projects based on dynamic models have supposed a significant advance in front of ...

La gestión de proyectos se puede considerar todavía como un arte en el cual el uso de la información cuantitativa tiende ...

This paper presents a new approach to data set editing. The algorithm (EOP: Editing by Ordered Projection) has some ...

In this last decade, association rules are being, inside Data Mining techniques, one of the most used tools to find ...

The use of dynamic models and simulation environments in connection with software projects paved the way for tools that ...

La gestión de proyectos se puede considerar todavía como un arte en el cual el uso de la información cuantitativa tiende ...

The aim of this paper is to describe a study for the obtaining, symbolically, of the separation surfaces between clusters ...

The k-Nearest Neighbor algorithm (k-NN) uses a classification criterion that depends on the parameter k. Usually, the value ...

A method for obtaining coordinated motion plans of robot manipulators is presented. A decoupled planning approach has been ...

Los Simuladores de Proyectos de Desarrollo de Software basados en modelos dinámicos han supuesto un avance significativo ...

This paper describes a new approach, HIDER (HIerarchical DEcision Rules), for learning rules in continuous and discrete ...

A method based on Evolutionary Algorithms for obtaining coordinated motion plans of multiple manipulator robots using a ...

A method based on genetic algorithms for obtaining coordinated motion plans of manipulator robots is presented. A decoupled ...

This article describes a new system for learning rules using rotated hyperboxes as individuals of a genetic algorithm (GA). ...

The process of determining whether a power system is in a secure or insecure state is a crucial task which must be addressed ...

The aim of this paper is to resume a great volume of quantitative knowledge in a qualitative model formed by linguistic ...

A method for obtaining coordinated motion plans of manipulator robots is presented. This planning can be easily implemented ...