@article {803,
	title = {Time Series Forecasting by Generalized Regression Neural Networks Trained With Multiple Series},
	journal = {IEEE Access},
	volume = {10},
	year = {2022},
	note = {PID2019-107793GB-I00},
	pages = {3275-3283},
	doi = {10.1109/ACCESS.2022.3140377},
	author = {Mart{\'\i}nez del Rio, Francisco and M.P. Fr{\'\i}as and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@conference {804,
	title = {Implementation of Data Stream Classification Neural Network Models Over Big Data Platforms},
	booktitle = {International Work-Conference on Artificial Neural Networks (IWANN 2021)},
	year = {2021},
	note = {TIN2015-68454-R, PID2019-107793GB-I00},
	pages = {272{\textendash}280},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	isbn = {978-3-030-85099-9},
	doi = {10.1007/978-3-030-85099-9_22},
	author = {F. Puentes and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and M. J. del Jesus}
}
@conference {780,
	title = {A Preliminary Study on Crop Classification with Unsupervised Algorithms for Time Series on Images with Olive Trees and Cereal Crops },
	booktitle = {15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)},
	year = {2020},
	note = {PID2019-107793GB-I00},
	month = {08/2020},
	pages = {276-285},
	abstract = {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 series are built from remote sensing images, and supervised techniques are widely used to classify them. However, one of the main drawbacks of these methods is the lack of labelled data sets to carry out the training process. Unsupervised classification has been less frequently used in this research field. The paper presents an experimental study comparing traditional clustering algorithms (with different dissimilarity measures) for the classification of olive trees and cereal crops from time series remote sensing data. The results obtained provide crucial information for developing novel and more accurate crop mapping algorithms.},
	keywords = {Clustering Crop mappings, Satellite imagery, Time series classification, Unsupervised learning},
	doi = {https://doi.org/10.1007/978-3-030-57802-2_27},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and D. Elizondo and Lipika Deka and M. J. del Jesus}
}
@article {767,
	title = {An analysis of technological frameworks for data streams},
	journal = {Progress in Artificial Intelligence},
	volume = {9},
	year = {2020},
	month = {06/2020},
	pages = {239{\textendash}261},
	abstract = {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, detailing their main characteristics. The objective is to facilitate decisions on which framework to use, meeting the needs of data mining methods for data streams. In this sense, there are important factors affecting the choice about which framework is most suitable for this purpose. Some of these factors are the existence of data mining libraries, the available documentation, the maturity of the platform, fault tolerance and processing guarantees, among others. Another decisive factor when choosing a data stream framework is its performance. For this reason, two comparisons have been made: a performance and latency comparison between Spark Streaming, Spark Structured Streaming, Storm, Flink and Samza following the Yahoo Streaming Benchmark methodology, and a comparison between Spark Streaming and Flink with a clustering algorithm for data streaming called streaming K-means.},
	keywords = {Big Data, Big data frameworks, Data stream engines, Data streaming, Technological frameworks},
	doi = {https://doi.org/10.1007/s13748-020-00210-6},
	author = {F. Puentes and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {777,
	title = {predtoolsTS: R package for streamlining time series forecasting},
	journal = {Progress in Artificial Intelligence},
	volume = {8},
	year = {2019},
	note = {TIN2015-68854-R},
	month = {06/2019},
	pages = {505{\textendash}510},
	abstract = {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 necessary to transform these time series. This paper presents predtoolsTS, an R package that provides a uniform interface for applying both statistical and ML methods to time series forecasting. predtoolsTS comprises four modules: preprocessing, modeling, prediction and postprocessing, in order to deal with the whole process of time series forecasting.},
	keywords = {machine learning, R, time series forecasting},
	doi = {https://doi.org/10.1007/s13748-019-00193-z},
	author = {Francisco Charte and Alberto Vico and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@conference {759,
	title = {An{\'a}lisis preliminar de marcos tecnol{\'o}gicos en data stream},
	booktitle = {II Workshop en Big Data y an{\'a}lisis de datos escalable},
	year = {2018},
	month = {10},
	pages = {1117-1122},
	address = {Granada (Espa{\~n}a)},
	author = {F. Puentes and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and M. J. del Jesus}
}
@article {MARTINEZ201838,
	title = {Dealing with seasonality by narrowing the training set in time series forecasting with kNN},
	journal = {Expert Systems with Applications},
	volume = {103},
	year = {2018},
	pages = {38 - 48},
	abstract = {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 examples whose targets belong to the season that is able to forecast. This way, the forecast of a specialized kNN learner is an aggregation of target values of the same season, reducing the likelihood of misleading forecasts. Although the strategy is applied to kNN, we think that other computational intelligence approaches could take advantage of it.},
	keywords = {NN regression, Seasonal time series, time series forecasting},
	issn = {0957-4174},
	doi = {https://doi.org/10.1016/j.eswa.2018.03.005},
	url = {http://www.sciencedirect.com/science/article/pii/S0957417418301441},
	author = {Francisco Mart{\'\i}nez and Mar{\'\i}a Pilar Fr{\'\i}as and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@article {298,
	title = {Nuevas arquitecturas hardware de procesamiento de alto rendimiento para aprendizaje profundo},
	journal = {Ense{\~n}anza y aprendizaje de ingenier{\'\i}a de computadores. Revista de experiencias docentes en ingenier{\'\i}a de computadores},
	volume = {8},
	year = {2018},
	note = {-},
	pages = {67{\textendash}83},
	abstract = {The design and manufacture of hardware is expensive, both in time and in economic investment, which is why integrated circuits are always manufactured in large volume, to take advantage of economies of scale. For this reason, the majority of processors manufactured are general purpose, thus expanding its range of applications. In recent years, however, more and more processors are being manufactured for specific applications, including those aimed at accelerating work with deep neural networks. This article introduces the need for this type of specialized hardware, describing its purpose, operation and current implementations.},
	issn = {2173-8688},
	author = {A.J. Rivera-Rivas and Francisco Charte and Espinilla, Macarena and M.D. P{\'e}rez-Godoy}
}
@article {Mart{\'\i}nez2017,
	title = {A methodology for applying k-nearest neighbor to time series forecasting},
	journal = {Artificial Intelligence Review},
	year = {2017},
	month = {Nov},
	abstract = {In this paper a methodology for applying k-nearest neighbor regression on a time series forecasting context is developed. The goal is to devise an automatic tool, i.e., a tool that can work without human intervention; furthermore, the methodology should be effective and efficient, so that it can be applied to accurately forecast a great number of time series. In order to be incorporated into our methodology, several modeling and preprocessing techniques are analyzed and assessed using the N3 competition data set. One interesting feature of the proposed methodology is that it resolves the selection of important modeling parameters, such as k or the input variables, combining several models with different parameters. In spite of the simplicity of k-NN regression, our methodology seems to be quite effective.},
	issn = {1573-7462},
	doi = {10.1007/s10462-017-9593-z},
	url = {https://doi.org/10.1007/s10462-017-9593-z},
	author = {Francisco Mart{\'\i}nez and Fr{\'\i}as, Mar{\'\i}a Pilar and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@article {289,
	title = {Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass},
	journal = {Computers \& Chemical Engineering},
	volume = {101},
	year = {2017},
	note = {ENE2014-60090-C2-2-R,TIN2015-68454-R},
	pages = {23{\textendash}30},
	abstract = {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 inputs, reducing the number of actual experiments to be completed. Response Surface Methodology (RSM) is one of the most common methods for this task, but machine learning algorithms represent an interesting alternative. In the present study the predictive performance of multiple models built from the same problem data are compared: the production of bioethanol from lignocellulosic materials. Four machine learning algorithms, including two neural networks, a support vector machine and a fuzzy system, together with the RSM method, are analyzed. Results show that Reg-CO2RBFN, the method designed by the authors, improves the results of all other alternatives.},
	doi = {10.1016/j.compchemeng.2017.02.008},
	author = {Francisco Charte and Inmaculada Romero and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Eulogio Castro}
}
@article {290,
	title = {Is the average photon energy a unique characteristic of the spectral distribution of global irradiance?},
	journal = {Solar Energy},
	volume = {149},
	year = {2017},
	note = {ENE2008-05098-ALT},
	pages = {32{\textendash}43},
	abstract = {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 global horizontal irradiance spectra. This paper basically applies the same methodology as that used in these studies, i.e., a statistical analysis based on spectral distributions grouped in 0.02-eV APE bins and their standard deviation across all 50-nm bands into which the wavelength range under scrutiny (350 to 1050 nm) is divided. Two years of spectral global tilted irradiance datasets collected at two Spanish locations, 333 km apart, are analyzed here.},
	doi = {10.1016/j.solener.2017.03.086},
	author = {Nofuentes, G and Gueymard, CA and J. Aguilera and M.D. P{\'e}rez-Godoy and Francisco Charte}
}
@article {602,
	title = {MEFASD-BD: Multi-Objective Evolutionary Algorithm for Subgroup Discovery in Big Data Environments - A MapReduce Solution},
	journal = {Knowledge-Based Systems},
	volume = {117},
	year = {2017},
	note = {TIN2015-68454-R},
	pages = {70-78},
	doi = {http://dx.doi.org/10.1016/j.knosys.2016.08.021},
	author = {F. Pulgar-Rubio and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and P. Gonz{\'a}lez and C. J. Carmona and M. J. del Jesus}
}
@conference {313,
	title = {Combining simple exponential smoothing models for time series forecasting},
	booktitle = {International work-conference on Time Series, ITISE 2016},
	year = {2016},
	note = {-},
	month = {6},
	pages = {635-644},
	address = {Granada (Spain)},
	abstract = {Simple exponentional smoothing is a well-known technique for forecasting univariate time series without trend and seasonality. Forecast combinations such as medians or means are known to improve the accuracy of point forecasts. In this paper we have experimented with combining the forecasts of several simple exponential smoothing models with different smoothing factors. Experimental results, using the M3-competition time series, show that the combined forecasts outperform the forecasts of the model that best fits the series.},
	author = {Francisco Mart{\'\i}nez and M.D. P{\'e}rez-Godoy and Francisco Charte and M. J. del Jesus}
}
@conference {10.1007/978-3-319-48799-1_8,
	title = {Recognition of Activities in Resource Constrained Environments; Reducing the Computational Complexity},
	booktitle = {Ubiquitous Computing and Ambient Intelligence},
	year = {2016},
	pages = {64{\textendash}74},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {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 computational complexity. These requirements are closely related to the size of the data used as input to NN. A wide range of prototype generation (PG) algorithms, which are designed for use with the NN approach, have been proposed in the literature to reduce the size of the data set. In this work, we investigate the use of PG algorithms and their effect on binary sensor-based activity recognition when using a NN approach. To identify the most suitable PG algorithm four datasets were used consisting of binary sensor data and their associated class activities. The results obtained demonstrated the potential of three PG algorithms for sensor-based activity recognition that reduced the computational complexity by up~to 95~{\%} with an overall accuracy higher than 90~{\%}.},
	isbn = {978-3-319-48799-1},
	author = {Espinilla, M. and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and Medina, J. and Mart{\'\i}nez, L. and Nugent, C.},
	editor = {Garc{\'\i}a, Carmelo R. and Caballero-Gil, Pino and Burmester, Mike and Quesada-Arencibia, Alexis}
}
@article {262,
	title = {The Influence of Noise on the Evolutionary Fuzzy Systems for Subgroup Discovery},
	journal = {Soft Computing},
	volume = {20},
	year = {2016},
	note = {TIN2015-68454-R, TIN2014-57251-P, P11-TIC-7765, P12-TIC-2958},
	pages = {4313-4330},
	doi = {10.1007/s00500-016-2300-1},
	author = {J. Luengo and A.M. Garc{\'\i}a-Vico and M.D. P{\'e}rez-Godoy and C. J. Carmona}
}
@conference {317,
	title = {An ensemble strategy for forecasting the extra-virgin olive oil price in Spain},
	booktitle = {International work-conference on Time Series, ITISE 2015},
	year = {2015},
	note = {TIN2012-33856},
	month = {7},
	pages = {506{\textendash}516},
	address = {Granada (Spain)},
	abstract = {Time series prediction is one of the key tasks in data mining, especially in areas such as science, engineering and business. It is possible to distinguish between fundamental analysis and technical analysis while dealing with time series in the business area. Fundamental analysis takes into account different exogenous variables such as expenses, assets or liabilities. Technical analysis summarizes information using technical indicators such as momentums, moving averages or oscillators. The most influential exogenous variables and technical indicators for the olive oil price have been already identified in previous studies. The objective of the present paper is to propose an ensemble strategy, based on dividing this set of exogenous variables and technical indicators into subsets of features for the base models. These base models use CO2RBFN, a cooperative competitive algorithm for RBFNs, as learning algorithm. The obtained results show that the ensemble strategy outperforms both the base models and other classical soft computing methods.},
	isbn = {978-84-16292-20-2},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and Francisco Charte and F. Pulgar-Rubio and M. J. del Jesus}
}
@conference {316,
	title = {CO2RBFN-CS: First Approach Introducing Cost-Sensitivity in the Cooperative-Competitive RBFN Design},
	booktitle = {13th International Work-Conference on Artificial Neural Networks (IWANN 2015)},
	year = {2015},
	note = {TIN2012-33856},
	month = {6},
	pages = {361{\textendash}373},
	address = {Palma de Mallorca (Spain)},
	abstract = {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 costs to wrong classification classes with the aim of reducing higher cost errors. In this paper, CO2RBFN-CS, a cooperative-competitive Radial Basis Function Network algorithm that implements cost-sensitivity is presented. Specifically, a cost parameter is introduced in the training stage of the algorithm. This parameter modifies the learning rate of the weights taking into account the right (or wrong) classification of the instance, the type of class (majority or minority) and the imbalance ratio of the data set.},
	isbn = {978-3-319-19257-4},
	doi = {10.1007/978-3-319-19258-1_31},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Francisco Charte and M. J. del Jesus}
}
@conference {655,
	title = {An ensemble method for time series forecasting with simple exponential smoothing},
	booktitle = {Conference Computational and Mathematical Methods in Science and Engineering},
	year = {2014},
	month = {07},
	address = {Rota, C{\'a}diz (Spain)},
	author = {M. J. del Jesus and Francisco Mart{\'\i}nez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fr{\'\i}as, Mar{\'\i}a Pilar}
}
@article {543,
	title = {Training algorithms for Radial Basis Function Networks to tackle learning processes with imbalanced data-sets},
	journal = {Applied Soft Computing},
	volume = {25},
	year = {2014},
	note = {TIN2012-33856},
	pages = {26-39},
	doi = {10.1016/j.asoc.2014.09.011},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and C. J. Carmona and M. J. del Jesus}
}
@conference {6706973,
	title = {A first analysis of the effect of local and global optimization weights methods in the cooperative-competitive design of RBFN for imbalanced environments},
	booktitle = {The 2013 International Joint Conference on Neural Networks (IJCNN)},
	year = {2013},
	month = {Aug},
	pages = {1-8},
	abstract = {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 modify or create new methods to address the imbalance problem. In this paper, CO2 RBFN a cooperative-competitive design method for Radial Basis Function Networks that has previously demonstrated a good behaviour tackling imbalanced data sets, is tested using two different training weights algorithms, local and global, in order to gain knowledge about this problem. As conclusions we can outline that a more global optimizer training algorithm obtains worse results.},
	keywords = {Accuracy, Algorithm design and analysis, algorithm-based approach, CO2RBFN, cooperative-competitive design method for radial basis function networks, data-based approach, global optimization weights methods, global optimizer training algorithm, imbalanced data sets, learning (artificial intelligence), Least squares approximations, local optimization weights methods, Neurons, optimisation, radial basis function networks, Sociology, Training, training weights algorithms},
	issn = {2161-4407},
	doi = {10.1109/IJCNN.2013.6706973},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. del Jesus and Francisco Mart{\'\i}nez}
}
@conference {323,
	title = {Alternative OVA Proposals for Cooperative Competitive RBFN Design in Classification Tasks},
	booktitle = {12th International Work-Conference on Artificial Neural Networks (IWANN 2013)},
	year = {2013},
	note = {TIN2012-33856,TIC-3928},
	pages = {331-338},
	address = {Tenerife (Spain)},
	abstract = {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 strategy used to combine the output of the binary classifiers is an interesting research area. In this paper different OVA strategies are developed and tested using as base classifier a cooperative-competitive RBFN design algorithm, CO2RBFN. One advantage of the obtained models is that they obtain as output for a given class a continuous value proportional to its level of confidence. Concretely three OVA strategies have been tested: the classical one, one based on the difference among outputs and another one based in a voting scheme, that has obtained the best results.},
	isbn = {978-3-642-38678-7},
	doi = {10.1007/978-3-642-38679-4_32},
	author = {Francisco Charte and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus}
}
@inbook {simidat230,
	title = {PRESETEMP: Predicci{\'o}n de Series Temporales mediante t{\'e}cnicas de Miner{\'\i}a de Datos},
	booktitle = {Proyectos de Investigaci{\'o}n 2009-10},
	year = {2012},
	isbn = {978-84-8439-642-0},
	author = {V. M. Rivas and E. Parras-Guti{\'e}rrez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. Olmo and M.G. Arenas and A. Fernandez-Montoya and M. Cillero},
	editor = {Eulogio Castro Galiano}
}
@article {Rivera2011,
	title = {A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods},
	journal = {Applied Intelligence},
	volume = {34},
	number = {3},
	year = {2011},
	month = {Jun},
	pages = {331{\textendash}346},
	abstract = {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 as the aim of this work. According to expert opinion, the use of exogenous variables and technical indicators can help in this task and must be included in the forecasting process. The amount of variables that can be considered makes necessary the use of feature selection algorithms in order to reduce the number of variables and to increase the interpretability and usefulness of the obtained forecasting system. Thus, in this paper CO2RBFN, a cooperative-competitive algorithm for Radial Basis Function Network design, and other soft computing methods have been applied to the data sets with the whole set of input variables and to the data sets with the selected set of input variables. The experimentation carried out shows that CO2RBFN obtains the best results in medium term forecasting for olive oil prices with the whole and with the selected set of input variables. Moreover, the feature selection methods applied to the data sets highlighted some influential variables which could be considered not only for the prediction but also for the description of the complex process involved in the medium-term forecasting of the olive oil price.},
	issn = {0924-669x},
	doi = {10.1007/s10489-011-0284-1},
	url = {https://doi.org/10.1007/s10489-011-0284-1},
	author = {A.J. Rivera-Rivas and P{\'e}rez-Recuerda, Pedro and M.D. P{\'e}rez-Godoy and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel}
}
@conference {10.1007/978-3-642-25274-7_27,
	title = {A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price},
	booktitle = {Advances in Artificial Intelligence},
	year = {2011},
	pages = {263{\textendash}272},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {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 ahead. The influential of the feature selection algorithms over the forecasting of the extra-virgin olive oil price has been analysed in this study and the results obtained with CO2RBFN have been compared with those obtained by different soft computing methods.},
	isbn = {978-3-642-25274-7},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus and P{\'e}rez-Recuerda, Pedro and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Lozano, Jose A. and G{\'a}mez, Jos{\'e} A. and Moreno, Jos{\'e} A.}
}
@conference {325,
	title = {Multi-label Testing for CO2RBFN: A First Approach to the Problem Transformation Methodology for Multi-label Classification},
	booktitle = {11th International Work-Conference on Artificial Neural Networks, IWANN 2011},
	year = {2011},
	note = {TIN2008-06681-C06-02,TIC-3928},
	month = {6},
	pages = {41{\textendash}48},
	address = {Torremolinos-M{\'a}laga (Spain)},
	abstract = {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 content. One way to address these applications is the Problem Transformation methodology that transforms the multi-label problem into one single-label classification problem, in order to apply traditional classification methods. The aim of this contribution is to test the performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs, in a multi-label environment, using the problem transformation methodology. The results obtained by CO2RBFN, and by other classical data mining methods, show that no algorithm outperforms the other on all the data.},
	isbn = {978-3-642-21501-8},
	doi = {10.1007/978-3-642-21501-8_6},
	author = {A.J. Rivera-Rivas and Francisco Charte and M.D. P{\'e}rez-Godoy and M. J. del Jesus}
}
@conference {586,
	title = {A Preliminary Study on Mutation Operators in Cooperative Competitive Algorithms for RBFN Design},
	booktitle = {IEEE World Congress on Computational Intelligence (WCCI)},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928},
	pages = {349{\textendash}355},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and C. J. Carmona and M. J. del Jesus}
}
@article {article,
	title = {Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets},
	journal = {Pattern Recognition Letters},
	volume = {31},
	number = {15},
	year = {2010},
	month = {11},
	pages = {2375-2388},
	issn = {0167-8655},
	doi = {10.1016/j.patrec.2010.07.010},
	author = {M.D. P{\'e}rez-Godoy and Fern{\'a}ndez, Alberto and Rivera Rivas, Antonio and M. J. del Jesus}
}
@inbook {587,
	title = {CO2RBFN for Short and Medium Term Forecasting of the Extra Virgin Olive Oil Price},
	booktitle = {Studies in Computational Intelligence},
	volume = {284},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928, UJA-08-16-30.},
	pages = {113-125},
	issn = {978-3-642-12538-6},
	author = {M.D. P{\'e}rez-Godoy and P. P{\'e}rez and M.P. Fr{\'\i}as and A.J. Rivera-Rivas and C. J. Carmona and M. Parras}
}
@article {582,
	title = {CO2RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market},
	journal = {International Journal of Hybrid Intelligent Systems},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928, UJA-08-16-30},
	pages = {75-87},
	doi = {10.3233/HIS-2010-0106},
	author = {M.D. P{\'e}rez-Godoy and P. P{\'e}rez and A.J. Rivera-Rivas and M. J. del Jesus and C. J. Carmona and M.P. Fr{\'\i}as and M. Parras}
}
@conference {10.1007/978-3-642-13022-9_21,
	title = {Intelligent Systems in Long-Term Forecasting of the Extra-Virgin Olive Oil Price in the Spanish Market},
	booktitle = {Trends in Applied Intelligent Systems},
	year = {2010},
	pages = {205{\textendash}214},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {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 of the implied organizations must be highlighted. In this paper an adaptation of CO2RBFN, evolutionary COoperative-COmpetitive algorithm for Radial Basis Function Networks design, applied to the long-term prediction of the extra-virgin olive oil price is presented. This long-term horizon has been fixed to six months. The results of CO2RBFN have been compared with other data mining methods, typically used in time series forecasting, such as other neural networks models, a support vector machine method and a fuzzy system.},
	isbn = {978-3-642-13022-9},
	author = {M.D. P{\'e}rez-Godoy and P{\'e}rez, Pedro and A.J. Rivera-Rivas and M. J. del Jesus and Fr{\'\i}as, Mar{\'\i}a Pilar and Parras, Manuel},
	editor = {Garc{\'\i}a-Pedrajas, Nicol{\'a}s and F. Herrera and Fyfe, Colin and Ben{\'\i}tez, Jos{\'e} Manuel and Ali, Moonis}
}
@conference {583,
	title = {Mejoras en el Dise{\~n}o Multiobjetivo de Redes de Funciones de Base Radial},
	booktitle = {Congreso Espa{\~n}ol sobre Tecnolog{\'\i}as y L{\'o}gica Fuzzy (ESTYLF)},
	year = {2010},
	note = {TIN2008-06681-C06-02, TIC-3928.},
	pages = {441-446},
	author = {P.L. L{\'o}pez and A.J. Rivera-Rivas and C. J. Carmona and M.D. P{\'e}rez-Godoy}
}
@conference {10.1007/978-3-642-02478-8_8,
	title = {A Preliminar Analysis of CO2RBFN in Imbalanced Problems},
	booktitle = {Bio-Inspired Systems: Computational and Ambient Intelligence},
	year = {2009},
	pages = {57{\textendash}64},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	address = {Berlin, Heidelberg},
	abstract = {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 performance of CO2RBFN, a cooperative-competitive evolutionary model for the design of RBFNs applied to classification problems on imbalanced domains and to study the cooperation of a well known preprocessing method, the {\textquoteleft}{\textquoteleft}Synthetic Minority Over-sampling Technique{\textquoteright}{\textquoteright} (SMOTE) with our algorithm. The good performance of CO2RBFN is shown through an experimental study carried out over a large collection of imbalanced data-sets.},
	isbn = {978-3-642-02478-8},
	author = {M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and Fern{\'a}ndez, A. and M. J. del Jesus and F. Herrera},
	editor = {Cabestany, Joan and Sandoval, Francisco and Prieto, Alberto and Corchado, Juan M.}
}
@conference {584,
	title = {Adaptaci{\'o}n de una asignatura avanzada de redes de computadores al modelo de docencia virtual dentro del marco del Espacio Europeo de Educaci{\'o}n Superior},
	booktitle = {International Conference on Development and Innovation with New Technologies in Engineering Education},
	year = {2009},
	pages = {15-21},
	author = {A.J. Rivera-Rivas and C. J. Carmona and M.D. P{\'e}rez-Godoy and M. J. del Jesus}
}
@article {article,
	title = {CO2RBFN: An evolutionary cooperative-competitive RBFN design algorithm for classification problems},
	journal = {Soft Computing},
	volume = {14},
	year = {2009},
	month = {07},
	pages = {953-971},
	doi = {10.1007/s00500-009-0488-z},
	author = {M.D. P{\'e}rez-Godoy and Rivera Rivas, Antonio and J. Berlanga, Francisco and M. J. del Jesus}
}
@conference {585,
	title = {EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design},
	booktitle = {International Work-Conference on Artificial Neural Networks (IWANN)},
	number = {2009},
	year = {2009},
	note = {TIN2008-06681-C06-02, TIN2007-60587},
	pages = {752{\textendash}759},
	publisher = {Lecture Notes in Computer Science 5517},
	organization = {Lecture Notes in Computer Science 5517},
	author = {P.L. L{\'o}pez and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy and M. J. del Jesus and C. J. Carmona}
}
@conference {4626756,
	title = {An Study on Data Mining Methods for Short-Term Forecasting of the Extra Virgin Olive Oil Price in the Spanish Market},
	booktitle = {2008 Eighth International Conference on Hybrid Intelligent Systems},
	year = {2008},
	month = {Sep.},
	pages = {943-946},
	abstract = {This paper presents the adaptation of an evolutionary cooperative competitive RBFN learning algorithm, CO2RBFN, 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, CO2RBFN. Results obtained has been compared with ARIMA models and other data mining methods such as a fuzzy system developed with a GA-P algorithm, a multilayer perceptron trained with a conjugate gradient algorithm and a radial basis function network trained with a LMS algorithm. The experimentation shows the high efficacy reached for the applied methods, specially for data mining methods which have slightly outperformed ARIMA methodology.},
	keywords = {Algorithm design and analysis, ARIMA, ARIMA models, Artificial neural networks, autoregressive moving average processes, CO2RBFN, conjugate gradient algorithm, conjugate gradient methods, data mining, data mining methods, evolutionary cooperative competitive, extra virgin olive oil price, Forecasting, fuzzy system, Fuzzy systems, GA-P algorithm, genetic algorithms, least mean squares methods, LMS algorithm, multilayer perceptron, multilayer perceptrons, Olive Oil Price, olive oil time series, Petroleum, pricing, radial basis function network, radial basis function networks, RBFN learning algorithm, short-term forecasting, Spanish market, time series, Time series analysis, time series forecasting, Training, vegetable oils},
	doi = {10.1109/HIS.2008.132},
	author = {P. P{\'e}rez and M. P. Fr{\'\i}as and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas and M. J. d. Jesus and M. Parras and F. J. Torres}
}
@conference {article,
	title = {Utilizaci{\'o}n de un sistema basado en reglas difusas para la aplicaci{\'o}n de operadores en un algoritmo cooperativo-competitivo},
	booktitle = {XIV Congreso Espa{\~n}ol sobre tecnolog{\'\i}as y logica fuzzy},
	year = {2008},
	month = {09},
	address = {Cuencas Mineras Asturianas},
	author = {M.D. P{\'e}rez-Godoy and Rivera Rivas, Antonio and M Jos{\'e}, Rivas and D{\'\i}az, Jesus and Jos{\'e}, F and Rivera, Berlanga}
}
@conference {inproceedings,
	title = {CoEvRBFN: An Approach to Solving the Classification Problem with a Hybrid Cooperative-Coevolutive Algorithm},
	year = {2007},
	month = {06},
	pages = {324-332},
	doi = {10.1007/978-3-540-73007-1_40},
	author = {M.D. P{\'e}rez-Godoy and Rivera Rivas, Antonio and M. J. del Jesus and Rojas, Ignacio}
}
@conference {719,
	title = {La asignatura de planificaci{\'o}n de sistemas inform{\'a}ticos en ingenier{\'\i}a t{\'e}cnica  en inform{\'a}tica de gesti{\'o}n.},
	booktitle = {Jenui},
	year = {2000},
	month = {01},
	address = {Alcala de Henares (Espa{\~n}a)},
	author = {P. Gonz{\'a}lez and M.D. P{\'e}rez-Godoy and A.J. Rivera-Rivas}
}
@conference {715,
	title = {Simulaci{\'o}n de fluidos din{\'a}micos mediante aut{\'o}matas celulares.},
	booktitle = {Congreso Espa{\~n}ol de inform{\'a}tica gr{\'a}fica.},
	year = {1999},
	month = {06},
	address = {Ja{\'e}n},
	author = {M.D. P{\'e}rez-Godoy and Andr{\'e}s Molina and Sanchez Pedro}
}
@conference {712,
	title = {Presentaci{\'o}n de TCL-TK para el desarrollo de aplicaciones por parte de usuarios no expertos en programaci{\'o}n},
	booktitle = {Jornadas cient{\'\i}ficas andaluzas en tecnolog{\'\i}a de la informaci{\'o}n},
	year = {1998},
	address = {C{\'a}diz (Espa{\~n}a)},
	author = {P. Gonz{\'a}lez and A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy}
}
@conference {710,
	title = {Herramientas para el desarrollo de pr{\'a}cticas en una asignatura de redes de computadores de una ingenier{\'\i}a t{\'e}cnica},
	booktitle = {Jornadas de inform{\'a}tica},
	year = {1997},
	address = {C{\'a}diz (Espa{\~n}a)},
	author = {A.J. Rivera-Rivas and M.D. P{\'e}rez-Godoy}
}
