Diseño. análisis y evaluación de conjuntos de clasificadores basados en redes de neuronas

Una de las áreas de investigación que, dentro del marco del Aprendizaje Automático, más atención ha recibido durante las últimas décadas ha sido el diseño de conjuntos de clasificadores. Bajo este denominador se engloban un gran número de algoritmos cuyo objetivo es la construcción de un clasificador robusto haciendo uso de clasificadores más simples denominados clasificadores base. Aunque el uso de los conjuntos de clasificadores se puede argumentar desde diversas perspectivas, la justificación más evidente se encuentra en el comportamiento humano. Antes de tomar una decisión importante es habitual pedir opinión a varios expertos para así tener mayor certeza de que la opción elegida es la más adecuada. Diversos estudios han demostrado que el éxito de cualquier conjunto de clasificadores viene determinado por la precisión y la diversidad de los clasificadores que lo integran. En otras palabras, para que un conjunto de clasificadores mejore la precisión de cualquiera de sus miembros se requiere que éstos sean precisos y diversos. Sin embargo, encontrar clasificadores base que, de forma simultánea, satisfagan ambos requisitos no es una tarea fácil. Por ello, en este trabajo se presentan dos nuevas arquitecturas de conjuntos de clasificadores en una de las cuales, sin obviar la diversidad, se fomenta la precisión de los clasificadores base, mientras que en la otra se fomenta la diversidad frente a la precisión. Las diferencias y la complementariedad existente entre ambas arquitecturas permitirá analizar la influencia que, en el comportamiento global del conjunto, tiene la primacía de una de estas particularidades frente a la otra. Aunque, en el mundo real, la mayor parte de los problemas de clasificación engloban a más de dos categorías, muchos de los conjuntos de clasificadores propuestos en la Bibliografía fueron originalmente concebidos para resolver problemas dicotómicos. En ocasiones, el algoritmo que rige el comportamiento de estos modelos puede extrapolarse a problemas multiclase. Sin embargo, en otros muchos casos, el problema multiclase sólo se puede resolver descomponiendo el problema original en subproblemas binarios. Además, la mayor parte de los modelos propuestos, han sido evaluados sobre dominios artificiales en los que el número de atributos con los que se describen los ejemplos es relativamente pequeño. A pesar de esta tendencia, existen un gran número de dominios reales en los que los ejemplos están descritos por cientos o incluso miles de características. La necesidad de disponer de nuevos métodos de clasificación capaces de resolver problemas reales marca uno de los objetivos de esta Tesis Doctoral. Así, las arquitecturas que se proponen en este trabajo han sido concebidas explícitamente para la resolución de problemas en los que el número de categorías es finito y superior a dos y en los que los ejemplos están descritos por un elevado número de atributos. Partiendo de estas dos singularidades, se pretende acotar, en la medida de lo posible, la complejidad y el coste computacional inherentes a la resolución de este tipo de problemas. La viabilidad de las arquitecturas propuestas se ha determinado experimentalmente. Así, el estudio realizado contempla un exhaustivo análisis en el que, sobre distintos dominios, se analiza el comportamiento de las arquitecturas propuestas y se compara con el logrado por algunos de los modelos de clasificación más referenciados en la Bibliografía. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------The design of Ensemble of Classifiers has been one of the most active research areas in the field of Machine Learning for the last decades. In this area, many different algorithms have been proposed in order to create a more robust classifier that consists of simpler classifiers named base classifiers. Although the use of ensemble of classifiers can be justified by many different reasons, the most obvious justification is related with human decision making process. Before making a decision, it is common to ask several experts to be sure that the chosen option is the optimal. Many studies have demonstrated that the success of any ensemble of classifiers is related to the accuracy and diversity of the different base classifiers of the ensemble. In other words, an ensemble of classifiers could improve the accuracy of any of its individual members if they are accurate and diverse. However, obtaining base classifiers which satisfy both requirements simultaneously is not an easy task. For this reason, this work presents two new ensembles of classifiers: One of these ensembles prioritizes the accuracy of the base classifiers (taking also into account the diversity) and the other promotes diversity over accuracy. These ensembles are different but complement each other, so it will be possible to analyze the influence of these requirements over the global performance of the ensemble. The number of applications that require multiclass categorization is huge in the real world. However, many of the studies related to supervised learning are focused on the resolution of binary problems. Some machine learning algorithms can then be naturally extended to handle the multiclass case. For other algorithms, a direct extension to the multiclass case may be problematic. Typically, in such cases, the multiclass problem is reduced to multiple binary classification problems that can be solved separately. In addition, most of these models have been evaluated in artificial domains in which the number of features used to describe the examples is relatively small. Despite this, there are many real domains in which the examples are described by hundreds or even thousands of features. For this reason, one of the goals of this thesis is the creation of new classification methods for real world. Thus, the ensembles proposed in this work have been designed to be applicable to real domains in which each example is labeled with one of several categories and is described by a large number of features. Taking these characteristics into account, the computational complexity and cost of the proposed methods need to be reduced as much as possible. The viability of the proposed ensembles has been proved empirically. Thus, this thesis makes a comprehensive analysis in which, taking into account different domains, the performance of the proposed ensembles is analyzed and compared with other wellknown classification methods

Mining Disease Courses across Organizations: A Methodology Based on Process Mining of Diagnosis Events Datasets

Berlín (Alemania) (23-27 julio 2019)This work was supported in part by grants TRA2015-63708-R and TRA2016-78886-C3-1-R (Spanish Government) and Topus (Madrid Regional Government)

CCE: An ensemble architecture based on coupled ANN for solving multiclass problems

The resolution of multiclass classification problems has been usually addressed by using a "divide and conquer" strategy that splits the original problem into several binary subproblems. This approach is mandatory when the learning algorithm has been designed to solve binary problems and a multiclass version cannot be devised. Artificial Neural Networks, ANN, are binary learning models whose extension to multiclass problems is rather straightforward by using the standard 1-out-of N codification of the classes. However, the use of a single ANN can be inefficient in terms of accuracy and computational complexity when the data set is large, or the number of classes is high. In this work, we exhaustively describe CCE, a new classifier ensemble based on ANN. Each member of this new ensemble is a couple of multiclass ANN's. Each ANN is trained using different subsets of the dataset ensuring these subsets to be disjoint. This new approach allows to combine the benefits of the divide and conquer methodology, with the use of multiclass ANNs and with the combination of individual classification modules that give a complete answer to the addressed problem. The combination of these elements results in a classifier ensemble in which the diversity of the base classifiers provides high accuracy values. Moreover, the use of couples of ANN proves to be tolerant to labeling noise and computationally efficient. The performance of CCE has been tested on various datasets and the results show the higher performance of this approach with respect to other used classification systems.This research was supported by the Spanish MINECO under projects TRA2016-78886-C3-1-R and RTI2018-096036-B-C22

Combining additive input noise annealing and pattern transformations for improved handwritten character recognition

Two problems that burden the learning process of Artificial Neural Networks with Back Propagation are the need of building a full and representative learning data set, and the avoidance of stalling in local minima. Both problems seem to be closely related when working with the handwritten digits contained in the MNIST dataset. Using a modest sized ANN, the proposed combination of input data transformations enables the achievement of a test error as low as 0.43%, which is up to standard compared to other more complex neural architectures like Convolutional or Deep Neural Networks. © 2014 Elsevier Ltd. All rights reserved.This research reported has been supported by the Spanish MICINN under projects TRA2010-20225-C03-01, TRA 2011-29454-C03-02, and TRA 2011-29454-C03-03

Lane following learning based on semantic segmentation with chroma key and image superposition

There are various techniques to approach learning in autonomous driving; however, all of them suffer from some problems. In the case of imitation learning based on artificial neural networks, the system must learn to correctly identify the elements of the environment. In some cases, it takes a lot of effort to tag the images with the proper semantics. This is also relevant given the need to have very varied scenarios to train and to thus obtain an acceptable generalization capacity. In the present work, we propose a technique for automated semantic labeling. It is based on various learning phases using image superposition combining both scenarios with chromas and real indoor scenarios. This allows the generation of augmented datasets that facilitate the learning process. Further improvements by applying noise techniques are also studied. To carry out the validation, a small-scale car model is used that learns to automatically drive on a reduced circuit. A comparison with models that do not rely on semantic segmentation is also performed. The main contribution of our proposal is the possibility of generating datasets for real indoor scenarios with automatic semantic segmentation, without the need for endless human labeling tasks.This work was supported by the Spanish Government under projects PID2019-104793RBC31/ AEI/10.13039/501100011033, RTI2018-096036-B-C22/AEI/10.13039/501100011033, TRA2016- 78886-C3-1-R/AEI/10.13039/501100011033, and PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17)

Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images

This work presents the development of an ADAS (advanced driving assistance system) focused on driver drowsiness detection, whose objective is to alert drivers of their drowsy state to avoid road traffic accidents. In a driving environment, it is necessary that fatigue detection is performed in a non-intrusive way, and that the driver is not bothered with alarms when he or she is not drowsy. Our approach to this open problem uses sequences of images that are 60 s long and are recorded in such a way that the subject’s face is visible. To detect whether the driver shows symptoms of drowsiness or not, two alternative solutions are developed, focusing on the minimization of false positives. The first alternative uses a recurrent and convolutional neural network, while the second one uses deep learning techniques to extract numeric features from images, which are introduced into a fuzzy logic-based system afterwards. The accuracy obtained by both systems is similar: around 65% accuracy over training data, and 60% accuracy on test data. However, the fuzzy logic-based system stands out because it avoids raising false alarms and reaches a specificity (proportion of videos in which the driver is not drowsy that are correctly classified) of 93%. Although the obtained results do not achieve very satisfactory rates, the proposals presented in this work are promising and can be considered a solid baseline for future works.This work was supported by the Spanish Government under projects PID2019- 104793RB-C31, TRA2016-78886-C3-1-R, RTI2018-096036-B-C22, PEAVAUTO-CM-UC3M and by the Region of Madrid’s Excellence Program (EPUC3M17)

A new artificial neural network ensemble based on feature selection and class recoding

Many of the studies related to supervised learning have focused on the resolution of multiclass problems. A standard technique used to resolve these problems is to decompose the original multiclass problem into multiple binary problems. In this paper, we propose a new learning model applicable to multi-class domains in which the examples are described by a large number of features. The proposed model is an Artificial Neural Network ensemble in which the base learners are composed by the union of a binary classifier and a multiclass classifier. To analyze the viability and quality of this system, it will be validated in two real domains: traffic sign recognition and hand-written digit recognition. Experimental results show that our model is at least as accurate as other methods reported in the bibliography but has a considerable advantage respecting size, computational complexity, and running tim

Implementing a Gaze Tracking Algorithm for Improving Advanced Driver Assistance Systems

Car accidents are one of the top ten causes of death and are produced mainly by driver distractions. ADAS (Advanced Driver Assistance Systems) can warn the driver of dangerous scenarios, improving road safety, and reducing the number of traffic accidents. However, having a system that is continuously sounding alarms can be overwhelming or confusing or both, and can be counterproductive. Using the driver"s attention to build an efficient ADAS is the main contribution of this work. To obtain this 'attention value” the use of a Gaze tracking is proposed. Driver"s gaze direction is a crucial factor in understanding fatal distractions, as well as discerning when it is necessary to warn the driver about risks on the road. In this paper, a real-time gaze tracking system is proposed as part of the development of an ADAS that obtains and communicates the driver"s gaze information. The developed ADAS uses gaze information to determine if the drivers are looking to the road with their full attention. This work gives a step ahead in the ADAS based on the driver, building an ADAS that warns the driver only in case of distraction. The gaze tracking system was implemented as a model-based system using a Kinect v2.0 sensor and was adjusted on a set-up environment and tested on a suitable-features driving simulation environment. The average obtained results are promising, having hit ratios between 96.37% and 81.84%This work has been supported by the Spanish Government under projects TRA2016-78886-C3-1-R, PID2019-104793RB-C31, RTI2018-096036-B-C22, PEAVAUTO-CM-UC3M and by the Region of Madrid Excellence Program (EPUC3M17

An ensemble approach of dual base learners for multi-class classification problems

In this work, we formalise and evaluate an ensemble of classifiers that is designed for the resolution of multi-class problems. To achieve a good accuracy rate, the base learners are built with pairwise coupled binary and multi-class classifiers. Moreover, to reduce the computational cost of the ensemble and to improve its performance, these classifiers are trained using a specific attribute subset. This proposal offers the opportunity to capture the advantages provided by binary decomposition methods, by attribute partitioning methods, and by cooperative characteristics associated with a combination of redundant base learners. To analyse the quality of this architecture, its performance has been tested on different domains, and the results have been compared to other well-known classification methods. This experimental evaluation indicates that our model is, in most cases, as accurate as these methods, but it is much more efficient. (C) 2014 Elsevier B.V. All rights reserved.This research was supported by the Spanish MICINN under Projects TRA2010-20225-C03-01, TRA 2011-29454-C03-02, and TRA 2011-29454-C03-03

Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles

Ensembles of classifiers is a proven approach in machine learning with a wide variety of research works. The main issue in ensembles of classifiers is not only the selection of the base classifiers, but also the combination of their outputs. According to the literature, it has been established that much is to be gained from combining classifiers if those classifiers are accurate and diverse. However, it is still an open issue how to define the relation between accuracy and diversity in order to define the best possible ensemble of classifiers. In this paper, we propose a novel approach to evaluate the impact of the diversity of the learners on the generation of heterogeneous ensembles. We present an exhaustive study of this approach using 27 different multiclass datasets and analysing their results in detail. In addition, to determine the performance of the different results, the presence of labelling noise is also considered.This work has been supported under projects PEAVAUTO-CM-UC3M–2020/00036/001, PID2019-104793RB-C31, and RTI2018-096036-B-C22, and by the Region of Madrid’s Excellence Program, Spain (EPUC3M17)