Intelligent Systems Group

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Intelligent Systems Group

New impact factor publication -- International Journal of Intelligent Systems -- IJIS


Our group members, Jerónimo Hernández, Iñaki Inza and José Antonio Lozano have recently completed the following publication:

"Multi-dimensional learning from crowds: usefulness and application of expertise detection. International Journal of Intelligent Systems.

Abstract: Learning from crowds is a classification problem where the provided training instances are labeled by multiple (usually conflicting) annotators. In different scenarios of this problem, straightforward strategies show an astonishing performance. In this paper, we characterize the crowd scenarios where these basic strategies show a good behavior. As a consequence, this study allows to identify those scenarios where non-basic methods for combining the multiple labels are expected to obtain better results. In this context, we extend the learning from crowds paradigm to the multidimensional (MD) classification domain. Measuring the quality of the annotators, the presented EM-based method overcomes the lack of a fully reliable labeling for learning MD Bayesian network classifiers: As the expertise is identified and the contribution of the relevant annotators promoted, the model parameters are optimized. The good performance of our proposal is demonstrated throughout different sets of experiments.


Josu Ceberio -- PhD defense


Our group member Josu Ceberio will defend his PhD, titled "Solving Permutation Problems with Estimation of Distribution Algorithms and Extensions Thereof", on December the 12th, at 10:30 in the main hall of the Computer Science Faculty, UPV-EHU.

The tribunal composition is the following: J.A. Gámez (Universidad de Castilla-La Mancha), C. Blum (Ikerbasque researcher), J. Mccall (Robert Gordon University, Aberdeen), M.J. Del Jesús (Universidad de Jaén) and B. Calvo (UPV-EHU).

The supervisors of the work are J.A. Lozano and A. Mendiburu.

The PhD document can be found here.


New impact factor publication -- Ecological Informatics


With the leadeship of our former member Jose A. Fernandes, our group members, José Antonio Lozano, Aritz Pérez and Inaki Inza,  and two marine science researchers (Xabier Irigoien and Nerea Goikoetxea), recently completed the following publication:

"Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species". Ecological Informatics, 25, 35-42. Year 2015.

doi link:

Abstract:  The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.


New impact factor publication -- Environmental Modelling & Software


Our group members, Unai López-Novoa, Alexander Mendiburu and José Miguel-Alonso, together with Jon Sáenz, Iñigo Errasti, Ganix Esnaola, Agustín Ezcurra and Gabriel Ibarra-Berastegi, researchers of the EOLO Group, recently completed the following publication:

"Multi-objective environmental model evaluation by means of multidimensional kernel density estimators: Efficient and multi-core implementations". Environmental Modelling & Software

doi link:

Abstract: We propose an extension to multiple dimensions of the univariate index of agreement between Probability Density Functions (PDFs) used in climate studies. We also provide a set of high-performance programs targeted both to single and multi-core processors. They compute multivariate PDFs by means of kernels, the optimal bandwidth using smoothed bootstrap and the index of agreement between multidimensional PDFs. Their use is illustrated with two case-studies. The first one assesses the ability of seven global climate models to reproduce the seasonal cycle of zonally averaged temperature. The second case study analyzes the ability of an oceanic reanalysis to reproduce global Sea Surface Temperature and Sea Surface Height. Results show that the proposed methodology is robust to variations in the optimal bandwidth used. The technique is able to process multivariate datasets corresponding to different physical dimensions. The methodology is very sensitive to the existence of a bias in the model with respect to observations.


Ekhiñe Irurozki -- PhD defense


Our group member Ekhiñe Irurozki will defend her PhD on October the 31st, friday, in the Computer Science Faculty, San Sebastian, 11:30AM. The title of the PhD is "Sampling and Learning Distance-based Probability Models for Permutation Spaces". PhD advisors are Jose A. Lozano and Borja Calvo.

A link to the PhD document can be found here.


New impact factor publication -- Weather and Forecasting


Our group members Pablo Rozas-Larraondo, Inaki Inza and Jose Antonio Lozano recently completed the following publication:

"A method for wind speed forecasting in airports based on non-parametric regression". Weather and Forecasting.


Wind is one of the parameters best predicted by numerical weather models, as it can be directly calculated from the physical equations of pressure which govern its movement. However, local winds are highly affected by topography which global numerical weather models, due to their limited resolution, are not able to reproduce. In order to improve the skill of numerical weather models, statistical and data analysis methods can be used. Machine learning techniques can be applied to train a model with data coming from both the model and observations in the area of interest. In this paper, a new method based on Non-parametric Multivariate Locally Weighted Regression is studied for improving the forecasted wind speed of a numerical weather model. Wind direction data is used to build different regression models, as a way of accounting for the effect of surrounding topography. The use of this technique offers similar levels of accuracy for wind speed forecasts compared with other machine learning algorithms with the advantage of being more intuitive and easy to interpret.
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