Intelligent Systems Group

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Intelligent Systems Group

An interview to Jonathan Ortigosa -- the role of machine learning in sentiment analysis


You can find an interesting interview to our group member Jonathan Ortigosa. The topic of the interview is the role of machine learning in the automatic classification of sentiment analysis of web posts, comments, etc.


Discovering Genes Involved in Disease and the Mystery of Missing Heritability


Tomorrow, 17th of June, Eleazar Eskin will give a talk at the Computer Science Faculty (San Sebastián).

Day: 17th of June

Hour: 11:00

Place: Seminar 3.1, Computer Science Faculty, Donostia - San Sebastián

Title: Discovering Genes Involved in Disease and the Mystery of Missing Heritability


It has been known for many decades that genetic differences among individuals account for a substantial por- tion of trait differences. Only recently has technology been developed to cost effectively measure genetic differences among individuals which creates the possibility of developing models that can predict disease related traits from an individual’s genetics. These models are a key component of personalized medicine and developing such models raise many computational challenges which provide important opportunities for Computer Scientists to make contributions toward human health.


Eleazar Eskin’s research focuses on developing computational methods for analysis of genetic variation.  He is currently a Professor in the Computer Science and Human Genetics departments at the University of California Los Angeles.  Previously, he was an Assistant Professor in Residence in Computer Science Engineering at the University of California, San Diego. Eleazar completed his Ph. D. in the Computer Science Department of Columbia University in New York City.  After graduation, he spent one year in the Computer Science Department at the Hebrew University in Jerusalem, Israel.


New impact factor publication -- Theoretical Computer Science


Our former group member Carlos Echegoyen, together with Roberto Santana, Alexander Mendiburu and Jose Antonio Lozano recently completed the following publication:

"Comprehensive characterization of the behaviors of estimation of distribution algorithms". Theoretical Computer Science.

Abstract: Estimation of distribution algorithms (EDAs) are a successful example of how to use machine learning techniques for designing robust and efficient heuristic search algorithms. Understanding the relationship between EDAs and the space of optimization problems is a fundamental issue for the successful application of this type of algorithms. A step forward in this matter is to create a taxonomy of optimization problems according to the different behaviors that an EDA can exhibit. This paper substantially extends previous work in the proposal of a taxonomy of problems for univariate EDAs, mainly by generalizing those results to EDAs that are able to deal with multivariate dependences among the variables of the problem. Through the definition of an equivalence relation between functions, it is possible to partition the space of problems into equivalence classes in which the algorithm has the same behavior. We provide a sufficient and necessary condition to determine the equivalence between functions. This condition is based on a set of matrices which provides a novel encoding of the relationship between the function and the probabilistic model used by the algorithm. The description of the equivalent functions belonging to a class is studied in depth for EDAs whose probabilistic model is given by a chordal Markov network. Assuming this class of factorization, we unveil the intrinsic connection between the behaviors of EDAs and neighborhood systems defined over the search space. In addition, we carry out numerical simulations that effectively reveal the different behaviors of EDAs for the injective functions defined over the search space {0,1}3. Finally, we provide a novel approach to extend the analysis of equivalence classes to non-injective functions.


New impact factor publication -- Applied Soft Computing


Our group members, Roberto Santana, Alexander Mendiburu and Jose Antonio Lozano recently completed the following publication:

"Multi-view classification of psychiatric conditions based on saccades". Applied Soft Computing.

Abstract: Early diagnosis of psychiatric conditions can be enhanced by taking into account eye movement behavior. However, the implementation of prediction algorithms which are able to assist physicians in the diagnostic is a difficult task. In this paper we propose, for the first time, an automatic approach for classification of multiple psychiatric conditions based on saccades. In particular, the goal is to classify 6 medical conditions: Alcoholism, Alzheimer's disease, opioid dependence (two groups of subjects with measurements respectively taken prior to and after administering synthetic opioid), Parkinson's disease, and Schizophrenia. Our approach integrates different feature spaces corresponding to complementary characterizations of the saccadic behavior. We define a multi-view model of saccades in which the feature representations capture characteristic temporal and amplitude patterns of saccades. Four of the current most advanced classification methods are used to discriminate among the psychiatric conditions and leave-one-out cross-validation is used to evaluate the classifiers. Classification accuracies well above the chance levels are obtained for the different classification tasks investigated. The confusion matrices reveal that it is possible to separate conditions into different groups. We conclude that using relatively simple descriptors of the saccadic behavior it is possible to simultaneously classify among 6 different types of psychiatric conditions. Conceptually, our multi-view classification method excels other approaches that focus on statistical differences in the saccadic behavior of cases and controls because it can be used for predicting unseen cases. Classification integrating different characterizations of the saccades can actually help to predict the conditions of new patients, opening the possibility to integrate automatic analysis of saccades as a practical procedure for differential diagnosis in Psychiatry.


New impact factor publication -- International Journal of High Performance Computing Applications


Our group members, Unai López-Novoa, Alexander Mendiburu and José Miguel-Alonso, together with Jon Sáenz, researcher of the EOLO Group, recently completed the following publication:

"An efficient implementation of kernel density estimation for multi-core and many-core architectures". International Journal of High Performance Computing Applications

doi link:

Abstract: Kernel density estimation (KDE) is a statistical technique used to estimate the probability density function of a sample set with unknown density function. It is considered a fundamental data-smoothing problem for use with large datasets, and is widely applied in areas such as climatology and biometry. Due to the large volumes of data that these problems usually process, KDE is a computationally challenging problem. Current HPC platforms with built-in accelerators have an enormous computing power, but they have to be programmed efficiently in order to take advantage of that power. We have developed a novel strategy to compute KDE using bounded kernels, trying to minimize memory accesses, and implemented it as a parallel program targeting multi-core and many-core processors. The efficiency of our code has been tested with different datasets, obtaining impressive levels of acceleration when taking as reference alternative, state-of-the-art KDE implementations.


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.

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