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6th International Conference on Neural Computation Theory and Applications, NCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Hervás-Martínez, C. University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus Albert Einstein Building Córdoba14071 Spain

ISBN: (纸本)9789897580543

Manifold learning covers those learning algorithms where high-dimensional data is assumed to lie on a low-dimensional manifold (usually nonlinear). Specific classification algorithms are able to preserve this manifold structure. On the other hand, ordinal regression covers those learning problems where the objective is to classify patterns into labels from a set of ordered categories. There have been very few works combining both ordinal regression and manifold learning. Additionally, privileged information refers to some special features which are available during classifier training, but not in the test phase. This paper contributes a new algorithm for combining ordinal regression and manifold learning, based on the idea of constructing a neighbourhood graph and obtaining the shortest path between all pairs of patterns. Moreover, we propose to exploit privileged information during graph construction, in order to obtain a better representation of the underlying manifold. The approach is tested with one synthetic experiment and 5 real ordinal datasets, showing a promising potential.

关键词： Learning algorithms

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Learning kernel label decompositions for ordinal classification problems 6

Learning kernel label decompositions for ordinal classificat...

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6th International Conference on Neural Computation Theory and Applications, NCTA 2014, Part of the 6th International Joint Conference on Computational Intelligence, IJCCI 2014

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Hervás-Martínez, C. University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus Albert Einstein Building Córdoba14071 Spain

ISBN: (纸本)9789897580543

This paper deals with the idea of decomposing ordinal multiclass classification problems when working with kernel methods. The kernel parameters are optimised for each classification subtask in order to better adjust the kernel to the data. More flexible multi-scale Gaussian kernels are considered to increase the goodness of fit of the kernel matrices. Instead of learning independent models for all the subtasks, the optimum convex combination of the kernel matrices is then obtained, leading to a single model able to better discriminate the classes in the feature space. The results of the proposed algorithm shows promising potential for the acquisition of better suited kernels.

关键词： Matrix algebra

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Whole-heart 4D flow can be acquired with preserved quality without respiratory gating facilitating clinical use

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Journal of Cardiovascular Magnetic Resonance 2015年第SUPPL 1期17卷 Q17-Q17页

作者： Mikael Kanski Johannes Töger Katarina Steding-Ehrenborg Christos G Xanthis Karin Markenroth Bloch Einar Heiberg Marcus Carlsson Håkan Arheden Cardiac MR group Lund Dept. of Clinical Physiology Lund University Lund Sweden Dept. of Numerical Analysis Center of Mathematical Sciences Lund University Lund Sweden Dept. of Computer Science and Biomedical informatics University of Thessaly Lamia Greece Philips Healthcare Lund Sweden Department of Biomedical Engineering Faculty of Engineering Lund University Lund Sweden

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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The firefighter problem: Application of hybrid ant colony optimization algorithms 14

The firefighter problem: Application of hybrid ant colony op...

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14th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2014

作者： Blum, Christian Blesa, Maria J García-Martínez, Carlos Rodríguez, Francisco J. Lozano, Manuel Dept. of Computer Science and Artifical Intelligence Univ. of the Basque Country UPV/EHU San Sebastian Spain IKERBASQUE Basque Foundation for Science Bilbao Spain ALBCOM Research Group Univ. Politécnica de Catalunya Barcelona Spain Dept. of Computing and Numerical Analysis Univ. of Córdoba Spain Dept. of Computer Science and Artificial Intelligence Univ. of Granada Spain

ISBN: (纸本)9783662443194

The firefigther problem is a deterministic discrete-time model for the spread (and the containment) of fire on an undirected graph. Assuming that the fire breaks out at a predefined set of vertices, the goal is to save as many vertices as possible from burning. The same model has also been used in the literature for the simulation of the spreading of deseases. In this work we present, to our knowledge, the first metaheuristics for tackling this problem. In particular, a pure ant colony optimization approach and a hybrid variant of this algorithm are proposed. The results show that the hybrid ant colony optimization variant is superior to the pure ant colony optimization version and to a mathematical programming solver, especially when the graph size and density grows. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Ant colony optimization

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Discovering subgroups by means of genetic programming

Discovering subgroups by means of genetic programming

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16th European Conference on Genetic Programming, EuroGP 2013

作者： Luna, José M. Romero, José Raúl Romero, Cristóbal Ventura, Sebastián Dept. of Computer Science and Numerical Analysis University of Cordoba Rabanales Campus 14071 Cordoba Spain

ISBN: (纸本)9783642372063

This paper deals with the problem of discovering subgroups in data by means of a grammar guided genetic programming algorithm, each subgroup including a set of related patterns. The proposed algorithm combines the requirements of discovering comprehensible rules with the ability of mining expressive and flexible solutions thanks to the use of a context-free grammar. A major characteristic of this algorithm is the small number of parameters required, so the mining process is easy for end-users. The algorithm proposed is compared with existing subgroup discovery evolutionary algorithms. The experimental results reveal the excellent behaviour of this algorithm, discovering comprehensible subgroups and behaving better than the other algorithms. The conclusions obtained were reinforced through a series of non-parametric tests. © 2013 Springer-Verlag.

关键词： Data mining

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Kernelizing the proportional odds model through the empirical kernel mapping

Kernelizing the proportional odds model through the empirica...

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12th International Work-Conference on Artificial Neural Networks, IWANN 2013

作者： Pérez-Ortiz, María Gutiérrez, Pedro Antonio Cruz-Ramírez, Manuel Sánchez-Monedero, Javier Hervás-Martínez, Cesar Dept. of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus 14071 Córdoba Spain

ISBN: (纸本)9783642386787

The classification of patterns into naturally ordered labels is referred to as ordinal regression. This paper explores the notion of kernel trick and empirical feature space in order to reformulate the most widely used linear ordinal classification algorithm (the Proportional Odds Model or POM) to perform nonlinear decision regions. The proposed method seems to be competitive with other state-of-the-art algorithms and significantly improves the original POM algorithm when using 8 ordinal datasets. Specifically, the capability of the methodology to handle nonlinear decision regions has been proven by the use of a non-linearly separable toy dataset. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Toys

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Evolutionary ordinal extreme learning machine

Evolutionary ordinal extreme learning machine

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8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013

作者： Sánchez-Monedero, Javier Gutiérrez, Pedro Antonio Hervás-Martínez, Cesar University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642408458

Recently the ordinal extreme learning machine (ELMOR) algorithm has been proposed to adapt the extreme learning machine (ELM) algorithm to ordinal regression problems (problems where there is an order arrangement between categories). In addition, the ELM standard model has the drawback of needing many hidden layer nodes in order to achieve suitable performance. For this reason, several alternatives have been proposed, such as the evolutionary extreme learning machine (EELM). In this article we present an evolutionary ELMOR that improves the performance of ELMOR and EELM for ordinal regression. The model is integrated in the differential evolution algorithm of EELM, and it is extended to allow the use of a continuous weighted RMSE fitness function which is proposed to guide the optimization process. This favors classifiers which predict labels as close as possible (in the ordinal scale) to the real one. The experiments include eight datasets, five methods and three specific performance metrics. The results show the performance improvement of this type of neural networks for specific metrics which consider both the magnitude of errors and class imbalance. © 2013 Springer-Verlag.

关键词： Regression analysis

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An n-spheres based synthetic data generator for supervised classification

An n-spheres based synthetic data generator for supervised c...

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12th International Work-Conference on Artificial Neural Networks, IWANN 2013

作者： Sánchez-Monedero, Javier Gutiérrez, Pedro Antonio Pérez-Ortiz, María Hervás-Martínez, César Dept. of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642386787

Synthetic datasets can be useful in a variety of situations, specifically when new machine learning models and training algorithms are developed or when trying to seek the weaknesses of an specific method. In contrast to real-world data, synthetic datasets provide a controlled environment for analysing concrete critic points such as outlier tolerance, data dimensionality influence and class imbalance, among others. In this paper, a framework for synthetic data generation is developed with special attention to pattern order in the space, data dimensionality, class overlapping and data multimodality. Variables such as position, width and overlapping of data distributions in the n-dimensional space are controlled by considering them as n-spheres. The method is tested in the context of ordinal regression, a paradigm of classification where there is an order arrangement between categories. The contribution of the paper is the full control over data topology and over a set of relevant statistical properties of the data. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Classification (of information)

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Borderline kernel based over-sampling

Borderline kernel based over-sampling

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8th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2013

作者： Pérez-Ortiz, María Gutiérrez, Pedro Antonio Hervás-Martínez, César University of Córdoba Dept. of Computer Science and Numerical Analysis Rabanales Campus 14071 - Córdoba Spain

ISBN: (纸本)9783642408458

Nowadays, the imbalanced nature of some real-world data is receiving a lot of attention from the pattern recognition and machine learning communities in both theoretical and practical aspects, giving rise to different promising approaches to handling it. However, preprocessing methods operate in the original input space, presenting distortions when combined with kernel classifiers, that operate in the feature space induced by a kernel function. This paper explores the notion of empirical feature space (a Euclidean space which is isomorphic to the feature space and therefore preserves its structure) to derive a kernel-based synthetic over-sampling technique based on borderline instances which are considered as crucial for establishing the decision boundary. Therefore, the proposed methodology would maintain the main properties of the kernel mapping while reinforcing the decision boundaries induced by a kernel machine. The results show that the proposed method achieves better results than the same borderline over- sampling method applied in the original input space. © 2013 Springer-Verlag.

关键词： Pattern recognition

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