Verification tools for probabilistic forecasts of continuous hydrological variables

In the present paper we describe some methods for verifying and evaluating probabilistic forecasts of hydrological variables. We propose an extension to continuous-valued variables of a verification method originated in the meteorological literature for the analysis of binary variables, and based on the use of a suitable cost-loss function to evaluate the quality of the forecasts. We find that this procedure is useful and reliable when it is complemented with other verification tools, borrowed from the economic literature, which are addressed to verify the statistical correctness of the probabilistic forecast. We illustrate our findings with a detailed application to the evaluation of probabilistic and deterministic forecasts of hourly discharge value

Development of computations in bioscience and bioinformatics and its application: review of the Symposium of Computations in Bioinformatics and Bioscience (SCBB06)

The first symposium of computations in bioinformatics and bioscience (SCBB06) was held in Hangzhou, China on June 21–22, 2006. Twenty-six peer-reviewed papers were selected for publication in this special issue of BMC Bioinformatics. These papers cover a broad range of topics including bioinformatics theories, algorithms, applications and tool development. The main technical topics contain gene expression analysis, sequence analysis, genome analysis, phylogenetic analysis, gene function prediction, molecular interaction and system biology, genetics and population study, immune strategy, protein structure prediction and proteomics

Frugal hypothesis testing and classification

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-175).The design and analysis of decision rules using detection theory and statistical learning theory is important because decision making under uncertainty is pervasive. Three perspectives on limiting the complexity of decision rules are considered in this thesis: geometric regularization, dimensionality reduction, and quantization or clustering. Controlling complexity often reduces resource usage in decision making and improves generalization when learning decision rules from noisy samples. A new margin-based classifier with decision boundary surface area regularization and optimization via variational level set methods is developed. This novel classifier is termed the geometric level set (GLS) classifier. A method for joint dimensionality reduction and margin-based classification with optimization on the Stiefel manifold is developed. This dimensionality reduction approach is extended for information fusion in sensor networks. A new distortion is proposed for the quantization or clustering of prior probabilities appearing in the thresholds of likelihood ratio tests. This distortion is given the name mean Bayes risk error (MBRE). The quantization framework is extended to model human decision making and discrimination in segregated populations.by Kush R. Varshney.Ph.D

A System for Induction of Oblique Decision Trees

This article describes a new system for induction of oblique decision trees. This system, OC1, combines deterministic hill-climbing with two forms of randomization to find a good oblique split (in the form of a hyperplane) at each node of a decision tree. Oblique decision tree methods are tuned especially for domains in which the attributes are numeric, although they can be adapted to symbolic or mixed symbolic/numeric attributes. We present extensive empirical studies, using both real and artificial data, that analyze OC1's ability to construct oblique trees that are smaller and more accurate than their axis-parallel counterparts. We also examine the benefits of randomization for the construction of oblique decision trees.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

Learning from data with uncertainty: Robust multiclass kernel-based classifiers and regressors.

Motivated by the presence of uncertainty in real data, in this research we investigate a robust optimization approach applied to multiclass support vector machines (SVMs) and support vector regression. Two new kernel based-methods are developed to address data with uncertainty where each data point is inside a sphere of uncertainty. For classification problems, the models are called robust SVM (R-SVM) and robust feasibility approach (R-FA) respectively as extensions of SVM approach. The two models are compared in terms of robustness and generalization error. For comparison purposes, the robust minimax probability machine (MPM) is applied and compared with the above methods. From the empirical results, we conclude that the R-SVM performs better than robust MPM. For regression problems, the models are called robust support vector regression (R-SVR) and robust feasibility approach for regression (R-FAR.). The proposed robust methods can improve the mean square error (MSE) in regression problems

SAR Target Image Generation Method Using Azimuth-Controllable Generative Adversarial Network

Sufficient synthetic aperture radar (SAR) target images are very important for the development of researches. However, available SAR target images are often limited in practice, which hinders the progress of SAR application. In this paper, we propose an azimuth-controllable generative adversarial network to generate precise SAR target images with an intermediate azimuth between two given SAR images' azimuths. This network mainly contains three parts: generator, discriminator, and predictor. Through the proposed specific network structure, the generator can extract and fuse the optimal target features from two input SAR target images to generate SAR target image. Then a similarity discriminator and an azimuth predictor are designed. The similarity discriminator can differentiate the generated SAR target images from the real SAR images to ensure the accuracy of the generated, while the azimuth predictor measures the difference of azimuth between the generated and the desired to ensure the azimuth controllability of the generated. Therefore, the proposed network can generate precise SAR images, and their azimuths can be controlled well by the inputs of the deep network, which can generate the target images in different azimuths to solve the small sample problem to some degree and benefit the researches of SAR images. Extensive experimental results show the superiority of the proposed method in azimuth controllability and accuracy of SAR target image generation

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms: support vector regression forecast combinations

The motivation of this paper is to introduce a hybrid Rolling Genetic Algorithm-Support Vector Regression (RG-SVR) model for optimal parameter selection and feature subset combination. The algorithm is applied to the task of forecasting and trading the EUR/USD, EUR/GBP and EUR/JPY exchange rates. The proposed methodology genetically searches over a feature space (pool of individual forecasts) and then combines the optimal feature subsets (SVR forecast combinations) for each exchange rate. This is achieved by applying a fitness function specialized for financial purposes and adopting a sliding window approach. The individual forecasts are derived from several linear and non-linear models. RG-SVR is benchmarked against genetically and non-genetically optimized SVRs and SVMs models that are dominating the relevant literature, along with the robust ARBF-PSO neural network. The statistical and trading performance of all models is investigated during the period of 1999–2012. As it turns out, RG-SVR presents the best performance in terms of statistical accuracy and trading efficiency for all the exchange rates under study. This superiority confirms the success of the implemented fitness function and training procedure, while it validates the benefits of the proposed algorithm

Genetic programming and serial processing for time series classification

This work describes an approach devised by the authors for time series classification. In our approach genetic programming is used in combination with a serial processing of data, where the last output is the result of the classification. The use of genetic programming for classification, although still a field where more research in needed, is not new. However, the application of genetic programming to classification tasks is normally done by considering the input data as a feature vector. That is, to the best of our knowledge, there are not examples in the genetic programming literature of approaches where the time series data are processed serially and the last output is considered as the classification result. The serial processing approach presented here fills a gap in the existing literature. This approach was tested in three different problems. Two of them are real world problems whose data were gathered for online or conference competitions. As there are published results of these two problems this gives us the chance to compare the performance of our approach against top performing methods. The serial processing of data in combination with genetic programming obtained competitive results in both competitions, showing its potential for solving time series classification problems. The main advantage of our serial processing approach is that it can easily handle very large datasets.Alfaro Cid, E.; Sharman, KC.; Esparcia Alcázar, AI. (2014). Genetic programming and serial processing for time series classification. Evolutionary Computation. 22(2):265-285. doi:10.1162/EVCO_a_00110S265285222Adeodato, P. J. L., Arnaud, A. L., Vasconcelos, G. C., Cunha, R. C. L. V., Gurgel, T. B., & Monteiro, D. S. M. P. (2009). The role of temporal feature extraction and bagging of MLP neural networks for solving the WCCI 2008 Ford Classification Challenge. 2009 International Joint Conference on Neural Networks. doi:10.1109/ijcnn.2009.5178965Alfaro-Cid, E., Merelo, J. J., de Vega, F. F., Esparcia-Alcázar, A. I., & Sharman, K. (2010). Bloat Control Operators and Diversity in Genetic Programming: A Comparative Study. Evolutionary Computation, 18(2), 305-332. doi:10.1162/evco.2010.18.2.18206Alfaro-Cid, E., Sharman, K., & Esparcia-Alcazar, A. I. (s. f.). Evolving a Learning Machine by Genetic Programming. 2006 IEEE International Conference on Evolutionary Computation. doi:10.1109/cec.2006.1688316Arenas, M. G., Collet, P., Eiben, A. E., Jelasity, M., Merelo, J. J., Paechter, B., … Schoenauer, M. (2002). A Framework for Distributed Evolutionary Algorithms. Lecture Notes in Computer Science, 665-675. doi:10.1007/3-540-45712-7_64Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T. M., Schalk, G., Wolpaw, J. R., … Birbaumer, N. (2004). The BCI Competition 2003: Progress and Perspectives in Detection and Discrimination of EEG Single Trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044-1051. doi:10.1109/tbme.2004.826692Borrelli, A., De Falco, I., Della Cioppa, A., Nicodemi, M., & Trautteur, G. (2006). Performance of genetic programming to extract the trend in noisy data series. Physica A: Statistical Mechanics and its Applications, 370(1), 104-108. doi:10.1016/j.physa.2006.04.025Eads, D. R., Hill, D., Davis, S., Perkins, S. J., Ma, J., Porter, R. B., & Theiler, J. P. (2002). Genetic Algorithms and Support Vector Machines for Time Series Classification. Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation V. doi:10.1117/12.453526Eggermont, J., Eiben, A. E., & van Hemert, J. I. (1999). A Comparison of Genetic Programming Variants for Data Classification. Lecture Notes in Computer Science, 281-290. doi:10.1007/3-540-48412-4_24Holladay, K. L., & Robbins, K. A. (2007). Evolution of Signal Processing Algorithms using Vector Based Genetic Programming. 2007 15th International Conference on Digital Signal Processing. doi:10.1109/icdsp.2007.4288629Kaboudan, M. A. (2000). Computational Economics, 16(3), 207-236. doi:10.1023/a:1008768404046Kishore, J. K., Patnaik, L. M., Mani, V., & Agrawal, V. K. (2000). Application of genetic programming for multicategory pattern classification. IEEE Transactions on Evolutionary Computation, 4(3), 242-258. doi:10.1109/4235.873235Kishore, J. K., Patnaik, L. M., Mani, V., & Agrawal, V. K. (2001). Genetic programming based pattern classification with feature space partitioning. Information Sciences, 131(1-4), 65-86. doi:10.1016/s0020-0255(00)00081-5Langdon, W. B., McKay, R. I., & Spector, L. (2010). Genetic Programming. International Series in Operations Research & Management Science, 185-225. doi:10.1007/978-1-4419-1665-5_7Yi Liu, & Khoshgoftaar, T. (s. f.). Reducing overfitting in genetic programming models for software quality classification. Eighth IEEE International Symposium on High Assurance Systems Engineering, 2004. Proceedings. doi:10.1109/hase.2004.1281730Luke, S. (2000). Two fast tree-creation algorithms for genetic programming. IEEE Transactions on Evolutionary Computation, 4(3), 274-283. doi:10.1109/4235.873237Luke, S., & Panait, L. (2006). A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation, 14(3), 309-344. doi:10.1162/evco.2006.14.3.309Mensh, B. D., Werfel, J., & Seung, H. S. (2004). BCI Competition 2003—Data Set Ia: Combining Gamma-Band Power With Slow Cortical Potentials to Improve Single-Trial Classification of Electroencephalographic Signals. IEEE Transactions on Biomedical Engineering, 51(6), 1052-1056. doi:10.1109/tbme.2004.827081Muni, D. P., Pal, N. R., & Das, J. (2006). Genetic programming for simultaneous feature selection and classifier design. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 36(1), 106-117. doi:10.1109/tsmcb.2005.854499Oltean, M., & Dioşan, L. (2009). An autonomous GP-based system for regression and classification problems. Applied Soft Computing, 9(1), 49-60. doi:10.1016/j.asoc.2008.03.008Otero, F. E. B., Silva, M. M. S., Freitas, A. A., & Nievola, J. C. (2003). Genetic Programming for Attribute Construction in Data Mining. Genetic Programming, 384-393. doi:10.1007/3-540-36599-0_36Poli, R. (2010). Genetic programming theory. Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO ’10. doi:10.1145/1830761.1830905Tsakonas, A. (2006). A comparison of classification accuracy of four genetic programming-evolved intelligent structures. Information Sciences, 176(6), 691-724. doi:10.1016/j.ins.2005.03.012Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., … Vaughan, T. M. (2000). Brain-computer interface technology: a review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 8(2), 164-173. doi:10.1109/tre.2000.84780

The effects of controlling language, fear, and disgust on responses to COVID-19 vaccination promotion messages

Using psychological reactance theory (PRT; Brehm, 1966) as an explanatory framework, this dissertation experimentally tested the effects of combining controlling language, fear appeals, and disgust appeals on responses to messages advocating COVID-19 vaccination. Measured responses included psychological reactance, source derogation, message attitudes, and COVID-19 vaccination intentions. Pilot study 1 (N = 240) was conducted to examine individual components (i.e., controlling language, fear appeals, disgust appeals) within the treatment messages and images. Pilot study 2 (N = 497) tested thirty-two treatment messages and selected sixteen to be used in the main study. A main study was then conducted to examine message responses. Participants (N = 447) were randomly assigned to one of eight conditions, crossing controlling language (high vs. low), fear appeals (high vs. low), and disgust appeals (high vs. low). In each condition, participants were presented with two message variations (counterbalanced) showing the consequences of COVID-19 infection and encouraging them to get vaccinated. After each message, participants were asked to respond to a battery of measures. Individual differences in trait reactance and disgust sensitivity were also measured prior to message exposure. Results showed when messages contained either high levels of controlling language, high fear, or high disgust appeals, message responses suffered—participants reported more source derogation and fewer favorable message attitudes, compared to messages containing either low levels of controlling language, low fear, or low disgust appeals. No significant 3-way interaction among controlling language, fear, and disgust appeals was found. However, the 2-way interaction between fear and disgust appeals in the low controlling language condition was significant—participants reported significantly less source derogation and more favorable message attitudes in the low controlling language, low fear, and low disgust appeals condition. Perceptions of freedom threat and reactance mediated the relationships between attribute-based message manipulations and source derogation, message attitudes, and vaccination intentions. Individual differences in trait reactance were positively associated with perceptions of freedom threat and reactance, but were negatively associated with source credibility, favorable message attitudes, and COVID-19 vaccination intentions. Individual differences in disgust sensitivity had no significant influence on message responses. The ramifications of these findings for enriching fear and disgust literature from a reactance perspective and encouraging COVID-19 vaccination as well as vaccinations against other viruses are noteworthy. Findings, limitations, and implications of the research are discussed. Keywords: COVID-19 vaccination, freedom threat, psychological reactance, message attitudes, source derogatio