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Class switching ensembles for ordinal regression

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10305 LNCS卷 408-419页

作者： Gutiérrez, Pedro Antonio Pérez-Ortiz, María Suárez, Alberto Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Quantitative Methods Universidad Loyola Andalucía Córdoba Spain Computer Science Department Universidad Autónoma de Madrid Madrid Spain

ISBN: (纸本)9783319591520

The term ordinal regression refers to classification tasks in which the categories have a natural ordering. The main premise of this learning paradigm is that the ordering can be exploited to generate more accurate predictors. The goal of this work is to design class switching ensembles that take into account such ordering so that they are more accurate in ordinal regression problems. In standard (nominal) class switching ensembles, diversity among the members of the ensemble is induced by injecting noise in the class labels of the training instances. Assuming that the classes are interchangeable, the labels are modified at random. In ordinal class switching, the ordering between classes is taken into account by reducing the transition probabilities to classes that are further apart. In this manner smaller label perturbations in the ordinal scale are favoured. Two different specifications of these transition probabilities are considered;namely, an arithmetic and a geometric decrease with the absolute difference of the class ranks. These types of ordinal class switching ensembles are compared with an ensemble method that does not consider class-switching, a nominal class-switching ensemble, an ordinal variant of boosting, and two state-of-the-art ordinal classifiers based on support vector machines and Gaussian processes, respectively. These methods are evaluated and compared in a total of 15 datasets, using three different performance metrics. From the results of this evaluation one concludes that ordinal class-switching ensembles are more accurate than standard class-switching ones and than the ordinal ensemble method considered. Furthermore, their performance is comparable to the state-of-the-art ordinal regression methods considered in the analysis. Thus, class switching ensembles with specifically designed transition probabilities, which take into account the relationships between classes, are shown to provide very accurate predictions in ordinal regression prob

关键词： Regression analysis

On the effect of local search in the multi-objective evolutionary discovery of software architectures

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On the effect of local search in the multi-objective evoluti...

Congress on Evolutionary Computation

作者： Aurora Ramírez José Raúl Romero Sebastián Ventura Universidad de Cordoba Cordoba AndalucÃa ES Dept. of Computer Science and Numerical Analysis University of Córdoba Campus of Rabanales Córdoba Spain

ISBN: (纸本)9781509046027

Software architects devote substantial efforts to find the most fitting architectural description for their system, which should not only specify its structure, but is also required to meet multiple, simultaneous quality criteria. Evolutionary computation has recently demonstrated to provide insightful support during the design phase by automatically deciding how to organise internal software components and how they should interact each other. Observed from a multi-objective perspective, particular care has to be taken in order to reach an appropriate trade-off among design metrics, while providing the software engineer with diverse alternatives to choose among. However, multi-objective evolutionary algorithms may find difficulties to control both aspects and, at the same time, to explore the entire search space in depth. Under these circumstances, local search can be applied to complement the evolution by scrutinising the most promising search directions. This paper proposes two different approaches that take advantage of the benefits of local search within the multi-objective evolutionary discovery of component-based software architectures. A detailed analysis and comparative study provides interesting findings like the importance of assigning a sufficient number of evaluations to the local improvement. The way in which local search explores and compares solutions for acceptance is a relevant aspect to promote diversity during the discovery process as well.

关键词： Software architecture Search problems computer architecture Memetics Evolutionary computation Measurement Mathematical model

A coral reef optimization algorithm for wave height time series segmentation problems

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10305 LNCS卷 673-684页

作者： Durán-Rosal, Antonio Manuel Guijo-Rubio, David Gutiérrez, Pedro Antonio Salcedo-Sanz, Sancho Hervás-Martínez, César Department of Computer Science and Numerical Analysis University of Córdoba Campus de Rabanales Córdoba14071 Spain Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Madrid28805 Spain

ISBN: (纸本)9783319591520

Time series segmentation can be approached using metaheuristics procedures such as genetic algorithms (GAs) methods, with the purpose of automatically finding segments and determine similarities in the time series with the lowest possible clusterwing error. In this way, segments belonging to the same cluster must have similar properties, and the dissimilarity between segments of different clusters should be the highest possible. In this paper we tackle a specific problem of significant wave height time series segmentation, with application in coastal and ocean engineering. The basic idea in this case is that similarity between segments can be used to characterise those segments with high significant wave heights, and then being able to predict them. A recently metaheuristic, the Coral Reef Optimization (CRO) algorithm is proposed for this task, and we analyze its performance by comparing it with that of a GA in three wave height time series collected in three real buoys (two of them in the Gulf of Alaska and another one in Puerto Rico). The results show that the CRO performance is better than the GA in this problem of time series segmentation, due to the better exploration of the search space obtained with the CRO. © Springer International Publishing AG 2017.

关键词： Genetic algorithms

Combining reservoir computing and over-sampling for ordinal wind power ramp prediction

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Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10305 LNCS卷 708-719页

作者： Dorado-Moreno, Manuel Cornejo-Bueno, Laura Gutiérrez, Pedro Antonio Prieto, Luis Salcedo-Sanz, Sancho Hervás-Martínez, César Department of Computer Science and Numerical Analysis Universidad de Córdoba Córdoba Spain Department of Signal Processing and Communications Universidad de Alcalá Alcalá de Henares Spain Department of Energy Resource Iberdrola Madrid Spain

ISBN: (纸本)9783319591520

Wind power ramp events (WPREs) are strong increases or decreases of wind speed in a short period of time. Predicting WPREs is of vital importance given that they can damage the turbines in a wind farm. In contrast to previous binary approaches (ramp versus non-ramp), a three-class prediction is proposed in this paper by considering: negative ramp, non-ramp and positive ramp, where the natural order of the events is clear. The independent variables used for prediction include past ramp function values and meteorological data obtained from physical models (reanalysis data). The proposed methodology is based on reservoir computing and an over-sampling process for alleviating the high degree of unbalance of the dataset (non-ramp events are much more frequent than ramps). The reservoir computing model is a modified echo state network composed by: a recurrent neural network layer, a nonlinear kernel mapping and an ordinal logistic regression, in such a way that the order of the classes can be exploited. The standard synthetic minority oversampling technique (SMOTE) is applied to the reservoir activations, given that the direct application over the input variables would damage its temporal structure. The performance of this proposal is compared to the original dataset (without over-sampling) and to nominal logistic regression, and the results obtained with the oversampled dataset and ordinal logistic regression are found to be more robust. © Springer International Publishing AG 2017.

关键词： Wind power

Variance-based robust optimization of a permanent magnet synchronous machine

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arXiv 2017年

作者： Putek, Piotr A. ter Maten, E.W. Jan Günther, Michael Sykulski, Jan K. Chair of Applied Mathematics and Numerical Analysis Bergische Universität Wuppertal Wuppertal42119 Germany Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

This paper focuses on the application of the variance-based global sensitivity analysis for a topology derivative method in order to solve a stochastic nonlinear time-dependent magnetoquasistatic interface problem. To illustrate the approach a permanent magnet synchronous machine has been considered. Our key objective is to provide a robust design of the rotor poles and of the tooth base in a stator for the reduction of the torque ripple and electromagnetic losses, while taking material uncertainties into account. Input variations of material parameters are modeled using the polynomial chaos expansion technique, which is incorporated into the stochastic collocation method in order to provide a response surface model. Additionally, we can benefit from the variance-based sensitivity analysis. This allows us to reduce the dimensionality of the stochastic optimization problems, described by the random-dependent cost functional. Finally, to validate our approach, we provide the two-dimensional simulations and analysis, which confirm the usefulness of the proposed method and yield a novel topology of a permanent magnet synchronous machine.65K10, 90C29, 49M37, 35R60 Copyright © 2017, The Authors. All rights reserved.

关键词： Random processes

Quasi-Monte-Carlo-based probabilistic assessment of wall heat loss

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Energy Procedia 2017年 132卷 705-710页

作者： Tianfeng Hou Dirk Nuyens Staf Roels Hans Janssen KU Leuven Department of Civil Engineering Building Physics Section Kasteelpark Arenberg 40 – box 2447 BE-3001 Heverlee Belgium KU Leuven Department of Computer Science Numerical Analysis and Applied Mathematics section Celestijnenlaan 200a - box 2402 BE-3001 Heverlee Belgium

In this paper, the potential of quasi-Monte Carlo methods for uncertainty propagation is assessed, via a case study of heat loss through a massive masonry wall. Four quasi-Monte Carlo sampling strategies – Optimized Latin hypercube, Sobol sequence, Niederreiter-Xing sequence and Good Lattice sequence – are applied and compared. Moreover, in order to terminate the quasi-Monte Carlo simulation when the desired accuracy is reached, an error estimation method is implemented. The outcomes show that all the four quasi-Monte Carlo methods outperform the standard Monte Carlo method; the Niederreiter-Xing sequence and Sobol sequence tend to be the best.

关键词： Quasi-Monte Carlo uncertainty propagation building performance sampling efficiency convergence monitoring

Complex dynamics, hidden attractors and continuous approximation of a fractional-order hyperchaotic PWC system

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arXiv 2018年

作者： Danca, Marius F. Fečkan, M. Kuznetsov, Nikolay V. Chen, Guanrong Department of Mathematics and Computer Science Avram Iancu University Cluj-Napoca400380 Romania Romanian Institute for Science and Technology Cluj-Napoca400487 Romania Department of Mathematical Analysis and Numerical Mathematics Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská dolina Bratislava842 48 Slovakia Mathematical Institute Slovak Academy of Sciences Štefánikova 49 Bratislava814 73 Slovakia Department of Applied Cybernetics Saint-Petersburg State University Russia Department of Mathematical Information Technology University of Jyväskylä Finland Department of Electronic Engineering City University of Hong Kong Hong Kong

In this paper, a continuous approximation to studying a class of PWC systems of fractionalorder is presented. Some known results of set-valued analysis and differential inclusions are utilized. The example of a hyperchaotic PWC system of fractional order is analyzed. It is found that without equilibria, the system has hidden attractors. Copyright © 2018, The Authors. All rights reserved.

关键词： Dynamical systems

Convergence analysis of energy conserving explicit local time-stepping methods for the wave equation

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arXiv 2017年

作者： Grote, Marcus J. Mehlin, Michaela Sauter, Stefan A. Department of Mathematics and Computer Science University of Basel Spiegelgasse 1 Basel4051 Switzerland Institute for Applied and Numerical Analysis Karlsruhe Institute of Technology Karlsruhe76131 Germany Institute for Mathematics University of Zurich Winterthurerstrasse 190 Zurich8057 Switzerland

Local adaptivity and mesh refinement are key to the efficient simulation of wave phenomena in heterogeneous media or complex geometry. Locally refined meshes, however, dictate a small time-step everywhere with a crippling effect on any explicit time-marching method. In [18] a leap-frog (LF) based explicit local time-stepping (LTS) method was proposed, which overcomes the severe bottleneck due to a few small elements by taking small time-steps in the locally refined region and larger steps elsewhere. Here a rigorous convergence proof is presented for the fully-discrete LTS-LF method when combined with a standard conforming finite element method (FEM) in space. numerical results further illustrate the usefulness of the LTS-LF Galerkin FEM in the presence of corner singularities.65M12, 65M20, 65M60, 65L06, 65L20 Copyright © 2017, The Authors. All rights reserved.

关键词： Wave propagation

The complexity of recognizing minimally tough graphs

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arXiv 2017年

作者： Katona, Gyula Y. Kovács, István Varga, Kitti Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary MTA-ELTE Numerical Analysis and Large Networks Research Group Hungary Shapr3D Hungary HAS Alfréd Rényi Institute of Mathematics Hungary

A graph is called t-tough if the removal of any vertex set S that disconnects the graph leaves at most |S|/t components. The toughness of a graph is the largest t for which the graph is t-tough. A graph is minimally t-tough if the toughness of the graph is t and the deletion of any edge from the graph decreases the toughness. The complexity class DP is the set of all languages that can be expressed as the intersection of a language in NP and a language in coNP. In this paper, we prove that recognizing minimally t-tough graphs is DP-complete for any positive rational number t. We introduce a new notion called weighted toughness, which has a key role in our *** Codes G.2.2, F.1.3 Copyright © 2017, The Authors. All rights reserved.

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