M. Dolores Pérez-Godoy

M. Dolores Pérez-Godoy
Universidad de Jaén | UJAEN · Department of Computer Sciences

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37
Publications
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244
Citations

Publications

Publications (37)
Article
With the current challenges in population growth and scarceness of food, new technologies are emerging. Remote sensing in general and satellite imagery more specifically are part of these technologies which can help provide accurate monitoring and classification of cultivars. Part of the increase in the use of these technologies has to do with the...
Article
Full-text available
Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this...
Chapter
Streaming is being increasingly demanded because it helps in analyzing data in real-time and in decision making. Over time, the number of existing devices increases continuously, generating a huge amount of data. Processing this data with traditional algorithms is impractical, so it is necessary to apply distributed algorithms in a Big Data context...
Chapter
Satellite imagery has been consolidated as an accurate option to monitor or classify crops. This is due to the continuous increase in spatial-temporal resolution and the availability of free access to this kind of services. In order to generate crop type maps (a valuable preprocessing step to most remote agriculture monitoring application), time se...
Article
Real-time data analysis is becoming increasingly important in Big Data environments for addressing data stream issues. To this end, several technological frameworks have been developed, both open-source and proprietary, for the analysis of streaming data. This paper analyzes some open-source technological frameworks available for data streams, deta...
Article
Full-text available
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 nec...
Article
Full-text available
In this paper, a new strategy for dealing with time series exhibiting a seasonal pattern is proposed. The strategy is applied in the context of time series forecasting using kNN regression. The key idea is to forecast every different season using a different specialized kNN learner. Each learner is specialized because its training set only contains...
Article
The average photon energy (APE) has become a popular index to qualitatively assess whether shorter or longer wavelengths are enhanced in a specific spectral distribution of irradiance when compared to the AM1.5G standard spectrum. According to some previous assessments, this index might uniquely distinguish individual global tilted irradiance and g...
Article
Nowadays, there is an incredible increase of data volumes around the world, with the Internet as one of the main actors in this scenario and a growth rate above 30GB/s. The treatment of this huge amount of information cannot be carried out through traditional data mining algorithms in an efficient way and it is necessary to adapt and design new alg...
Article
The production of biofuels is a process that requires the adjustment of multiple parameters. Performing experiments in which these parameters are changed and the outputs are analyzed is imperative, but the cost of these tests limits their number. For this reason, it is important to design models that can predict the different outputs with changing...
Conference Paper
In our current work we propose a strategy to reduce the vast amounts of data produced within smart environments for sensor-based activity recognition through usage of the nearest neighbor (NN) approach. This approach has a number of disadvantages when deployed in resource constrained environments due to its high storage requirements and computation...
Article
Full-text available
External factors such as the presence of noise in data can affect the data mining process. This is a common problem that produces several negative consequences which involves errors in the data collection, preparation and, above all, in the results obtained by the data mining techniques employed. The capabilities of the models built under such circ...
Conference Paper
The interest in dealing with imbalanced datasets has grown in the last years, since they represent many real world scenarios. Different methods that address imbalance problems can be classified into three categories: data sampling, algorithmic modification and cost-sensitive learning. The fundamentals of the last methodology is to assign higher cos...
Conference Paper
Full-text available
Many real applications are composed of data sets where the distribution of the classes is significantly different. These data sets are commonly known as imbalanced data sets. Proposed approaches that address this problem can be categorized into two types: data-based, which resample problem data in a preprocessing phase and algorithm-based which mod...
Conference Paper
Full-text available
In the Machine Learning field when the multi-class classification problem is addressed, one possibility is to transform the data set in binary data sets using techniques such as One-Versus-All. One classifier must be trained for each binary data set and their outputs combined in order to obtain the final predicted class. The determination of the st...
Conference Paper
In this paper we present a summary of the application of CO2RBFN, a evolutionary cooperative-competitive algorithm for Radial Basis Function Networks design, to the medium-term forecasting of the extra-virgen olive price, carry out by the SIMIDAT research group. The forecast is about the price at source of the extra-virgin olive oil six months ahea...
Article
Full-text available
Time series forecasting is an important task for the business sector. Agents involved in the olive oil sector consider that, for the olive oil price, medium-term predictions are more important than short-term predictions. In collaboration with these agents the forecasting of the price of extra-virgin olive oil six months ahead has been established...
Conference Paper
Full-text available
While in traditional classification an instance of the data set is only associated with one class, in multi-label classification this instance can be associated with more than one class or label. Examples of applications in this growing area are text categorization, functional genomics and association of semantic information to audio or video conte...
Article
In the classification problem field, we often encounter many real application areas in which the data do not have an equitable distribution among the different classes of the problem. In such cases, we are dealing with the so-called imbalanced data sets. This scenario has significant interest since standard classifiers are often biased towards the...
Conference Paper
Full-text available
Evolutionary Computation is a typical paradigm for the Radial Basis Function Network design. In this environment an individual represents a whole network. An alternative is to use cooperative-competitive methods where an individual is a part of the solution. CO<sup>2</sup>RBFN is an evolutionary cooperative-competitive hybrid methodology for the de...
Conference Paper
Full-text available
In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits...
Chapter
Full-text available
In this paper an adaptation of CO2RBFN, evolutionary COoperative- COmpetitive algorithm for Radial Basis Function Networks design, applied to the prediction of the extra-virgin olive oil price is presented. In this algorithm each individual represents a neuron or Radial Basis Function and the population, the whole network. Individuals compite for s...
Article
Full-text available
This paper presents the adaptation of CO 2 RBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra vir- gin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire popul...
Article
Full-text available
This paper presents a new evolutionary cooperative–competitive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, CO2RBFN, promotes a cooperative–competitive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final so...
Conference Paper
Full-text available
In this paper a multiobjective optimization algorithm for the design of Radial Basis Function Networks is proposed. The goal of the design algorithm is to obtain networks with a high tradeoff between accuracy and complexity, overcoming the drawbacks of the traditional single objective evolutionary algorithms. The main features of EMORBFN are a sele...
Conference Paper
Full-text available
In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the perform...
Conference Paper
Full-text available
This paper presents the adaptation of an evolutionary cooperative competitive RBFN learning algorithm, CO<sup>2</sup>RBFN, for short-term forecasting of extra virgin olive oil price. The olive oil time series has been analyzed with a new evolutionary proposal for the design of RBFNs, CO<sup>2</sup>RBFN. Results obtained has been compared with ARIMA...
Conference Paper
Full-text available
This paper presents a new cooperative-coevolutive algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm promotes a coevolutive environment where each individual represents a radial basis function (RBF) and the entire population is responsible for the final solution. As credit assignment three...
Article
Full-text available
Resumen En este trabajo se presenta CO 2 RBFN, un algoritmo bioinspirado para el diseño de redes neuronales, concretamente Redes de Funciones de Base Radial (RBFNs). El método de aprendizaje está basado en la programación evolutiva con un enfoque cooperativo-competitivo en el que cada individuo representa una neurona y la población al completo la r...
Article
Full-text available
Resumen En este artículo se presenta un nuevo elemento poblacional a considerar en el diseño de algoritmos evolutivos multiobjetivo para la optimización de Redes de Funciones de Base Radial. Concretamente, se divide la población en subpoblaciones virtuales, donde cada subpoblación está compuesta por individuos o redes con el mismo número de neurona...
Article
Full-text available
In this paper, the effect of the inclusion of a feature selection stage previous to the RBFNs design is analyzed. Two different RBFNs design algorithms have been used: a cooperative-competitive scheme, where each individual is a single neuron, and a Pittsburgh evolution-ary scheme, where each individual is a complete network. On the other hand, six...

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