Vote-boosting ensembles

Vote-boosting is a sequential ensemble learning method in which the individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners are used, emphasis should be made on instances for which the disagreement rate is high. When more flexible classifiers are used and as the noise level increases, the emphasis on these uncertain instances should be reduced. In fact, at sufficiently high levels of class-label noise, the focus should be on instances on which the ensemble classifiers agree. The optimal type of emphasis can be automatically determined using cross-validation. An extensive empirical analysis using the beta distribution as emphasis function illustrates that vote-boosting is an effective method to generate ensembles that are both accurate and robust

Ensemble learning in the presence of noise

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingenieria Informática. Fecha de lectura: 14-02-2019La disponibilidad de grandes cantidades de datos provenientes de diversas fuentes ampl a enormemente las posibilidades para una explotaci on inteligente de la informaci on. No obstante, la extracci on de conocimiento a partir de datos en bruto es una tarea compleja que requiere el desarrollo de m etodos de aprendizaje e cientes y robustos. Una de las principales di cultades en el aprendizaje autom atico es la presencia de ruido en los datos. En esta tesis, abordamos el problema del aprendizaje autom atico en presencia de ruido. Para este prop osito, nos centraremos en el uso de conjuntos de clasi cadores. Nuestro objetivo es crear colecciones de aprendices base cuyos resultados, al ser combinados, mejoren no solo la precisi on sino tambi en la robustez de las predicciones. Una primera contribuci on de esta tesis es aprovechar el ratio de submuestreo para construir conjuntos de clasi cadores basados en bootstrap (como bagging o random forests) precisos y robustos. La idea de utilizar el submuestreo como mecanismo de regularizaci on tambi en se explota para la detecci on de ejemplos ruidosos. En concreto, los ejemplos que est an mal clasi cados por una fracci on de los miembros del conjunto se marcan como ruido. El valor optimo de este umbral se determina mediante validaci on cruzada. Las instancias ruidosas se eliminan ( ltrado) o se corrigen sus etiquetas de su clase (limpieza). Finalmente, se construye un conjunto de clasi cadores utilizando los datos de entrenamiento limpios ( ltrados o limpiados). Otra contribuci on de esta tesis es vote-boosting, un m etodo de conjuntos secuencial especialmente dise~nado para ser robusto al ruido en las etiquetas de clase. Vote-boosting reduce la excesiva sensibilidad a este tipo de ruido de los algoritmos basados en boosting, como adaboost. En general, los algoritmos basados en booting modi can la distribuci on de pesos en los datos de entrenamiento progresivamente para enfatizar instancias mal clasi cadas. Este enfoque codicioso puede terminar dando un peso excesivamente alto a instancias cuya etiqueta de clase sea incorrecta. Por el contrario, en vote-boosting, el enfasis se basa en el nivel de incertidumbre (acuerdo o desacuerdo) de la predicci on del conjunto, independientemente de la etiqueta de clase. Al igual que en boosting, voteboosting se puede analizar como una optimizaci on de descenso por gradiente en espacio funcional. Uno de los problemas abiertos en el aprendizaje de conjuntos es c omo construir combinaciones de clasi cadores fuertes. La principal di cultad es lograr diversidad entre los clasi cadores base sin un deterioro signi cativo de su rendimiento y sin aumentar en exceso el coste computacional. En esta tesis, proponemos construir conjuntos de SVM con la ayuda de mecanismos de aleatorizaci on y optimizaci on. Gracias a esta combinaci on de estrategias complementarias, es posible crear conjuntos de SVM que son mucho m as r apidos de entrenar y son potencialmente m as precisos que un SVM individual optimizado. Por ultimo, hemos desarrollado un procedimiento para construir conjuntos heterog eneos que interpolan sus decisiones a partir de conjuntos homog eneos compuestos por diferentes tipos de clasi cadores. La composici on optima del conjunto se determina mediante validaci on cruzada. v

Ensemble Learning in the Presence of Noise

Learning in the presence of noise is an important issue in machine learning. The design and implementation of e ective strategies for automatic induction from noisy data is particularly important in real-world problems, where noise from defective collecting processes, data contamination or intrinsic uctuations is ubiquitous. There are two general strategies to address this problem. One is to design a robust learning method. Another one is to identify noisy instances and eliminate or correct them. In this thesis we propose to use ensembles to mitigate the negative impact of mislabelled data in the learning process. In ensemble learning the predictions of individual learners are combined to obtain a nal decision. E ective combinations take advantage of the complementarity of these base learners. In this manner the errors incurred by a learner can be compensated by the predictions of other learners in the combination. A rst contribution of this work is the use of subsampling to build bootstrap ensembles, such as bagging and random forest, that are resilient to class label noise. By using lower sampling rates, the detrimental e ect of mislabelled examples on the nal ensemble decisions can be tempered. The reason is that each labelled instance is present in a smaller fraction of the training sets used to build individual learners. Ensembles can also be used as a noise detection procedure to improve the quality of the data used for training. In this strategy, one attempts to identify noisy instances and either correct (by switching their class label) or discard them. A particular example is identi ed as noise if a speci ed percentage (greater than 50%) of the learners disagree with the given label for this example. Using an extensive empirical evaluation we demonstrate the use of subsampling as an e ective tool to detect and handle noise in classi cation problems

Improving the robustness of bagging with reduced sampling size

This is an electronic version of the paper presented at the 22th European Symposium on Artificial Neural Networks, held in Bruges on 2014Bagging is a simple and robust classification algorithm in the presence of class label noise. This algorithm builds an ensemble of classifiers by bootstrapping samples with replacement of size equal to the original training set. However, several studies have shown that this choice of sampling size is arbitrary in terms of generalization performance of the ensemble. In this study we discuss how small sampling ratios can contribute to the robustness of bagging in the presence of class label noise. An empirical analysis on two datasets is carried out using different noise rates and bootstrap sampling sizes. The results show that, for the studied datasets, sampling rates of 20% clearly improve the performance of the bagging ensembles in the presence of class label noise.The authors acknowledge financial support from the Spanish Dirección General de Investigación, project TIN2010-21575-C02-0

Small margin ensembles can be robust to class-label noise

This is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, VOL 160 (2015) DOI 10.1016/j.neucom.2014.12.086Subsampling is used to generate bagging ensembles that are accurate and robust to class-label noise. The effect of using smaller bootstrap samples to train the base learners is to make the ensemble more diverse. As a result, the classification margins tend to decrease. In spite of having small margins, these ensembles can be robust to class-label noise. The validity of these observations is illustrated in a wide range of synthetic and real-world classification tasks. In the problems investigated, subsampling significantly outperforms standard bagging for different amounts of class-label noise. By contrast, the effectiveness of subsampling in random forest is problem dependent. In these types of ensembles the best overall accuracy is obtained when the random trees are built on bootstrap samples of the same size as the original training data. Nevertheless, subsampling becomes more effective as the amount of class-label noise increases.The authors acknowledge financial support from Spanish Plan Nacional I+D+i Grant TIN2013-42351-P and from Comunidad de Madrid Grant S2013/ICE-2845 CASI-CAM-CM

Model-free neural network-based predictive control for robust operation of power converters

An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC

Building heterogeneous ensembles by pooling homogeneous ensembles

Heterogeneous ensembles consist of predictors of different types, which are likely to have different biases. If these biases are complementary, the combination of their decisions is beneficial and could be superior to homogeneous ensembles. In this paper, a family of heterogeneous ensembles is built by pooling classifiers from M homogeneous ensembles of different types of size T. Depending on the fraction of base classifiers of each type, a particular heterogeneous combination in this family is represented by a point in a regular simplex in M dimensions. The M vertices of this simplex represent the different homogeneous ensembles. A displacement away from one of these vertices effects a smooth transformation of the corresponding homogeneous ensemble into a heterogeneous one. The optimal composition of such heterogeneous ensemble can be determined using cross-validation or, if bootstrap samples are used to build the individual classifiers, out-of-bag data. The proposed heterogeneous ensemble building strategy, composed of neural networks, SVMs, and random trees (i.e. from a standard random forest), is analyzed in a comprehensive empirical analysis and compared to a benchmark of other heterogeneous and homogeneous ensembles. The achieved results illustrate the gains that can be achieved by the proposed ensemble creation method with respect to both homogeneous ensembles and to the tested heterogeneous building strategy at a fraction of the training cost.The authors acknowledge financial support from PID2019-106827GB-I00/AEI/10.13039/50110001103

Quality Assessment of Educational Services in Nursing and Midwifery School of Kerman Based on SERVQUAL Model

Introduction: One of the most important factors in the success and survival of the higher education system is attention to the quality of provided services. Therefore, the aim of this study was to evaluate the quality of educational services in the Razi faculty of nursing and midwifery of Kerman based on SERVQUAL model in 2014. Methods: In this descriptive-analytical study, 150 students of the faculty of nursing and midwifery were selected by simple random sampling method. The data collection tool was SERVQUAL standard questionnaire which included three parts of demographic information and 28 pairs of items measuring current and desired status of service quality. The mean difference between the current and desired status was calculated as service gap. The collected data were analyzed using descriptive and inferential statistics (paired t-test, independent t-test, Kruskal-Wallis, and ANOVA). Results: The results showed that a negative gap existed in all dimensions of the service quality with the maximum gap being in the physical and accountability dimensions (-1.7) and the minimum gap in the reliability dimension (-0.6). There was no statistically significant difference between the gap in quality of educational services in the five dimensions and gender and educational level this difference was only significant between the gap in quality of educational services in the five dimensions and field of study (p< 0.05). Conclusion: Given the negative gaps in the five dimensions of service quality and greater gaps in physical and accountability dimensions, it is recommended that resources be allocated by appropriate planning and prioritization and also training courses be offered for staff and faculty members about effective methods of presenting educational services and effective communication with students

Correction to

Funding Information: The authors acknowledge financial support from PID2019-106827GB-I00/AEI/10.13039/501100011033. Publisher Copyright: © Springer-Verlag GmbH Germany, part of Springer Nature 2021.Unfortunately, the article has been published without acknowledgment. The correct acknowledgment is given below. The authors acknowledge financial support from PID2019-106827GB-I00/AEI/10.13039/501100011033.Peer reviewe