102 research outputs found
On the design of an ECOC-compliant genetic algorithm
Genetic Algorithms (GA) have been previously applied to Error-Correcting Output Codes (ECOC) in state-of-the-art works in order to find a suitable coding matrix. Nevertheless, none of the presented techniques directly take into account the properties of the ECOC matrix. As a result the considered search space is unnecessarily large. In this paper, a novel Genetic strategy to optimize the ECOC coding step is presented. This novel strategy redefines the usual crossover and mutation operators in order to take into account the theoretical properties of the ECOC framework. Thus, it reduces the search space and lets the algorithm to converge faster. In addition, a novel operator that is able to enlarge the code in a smart way is introduced. The novel methodology is tested on several UCI datasets and four challenging computer vision problems. Furthermore, the analysis of the results done in terms of performance, code length and number of Support Vectors shows that the optimization process is able to find very efficient codes, in terms of the trade-off between classification performance and the number of classifiers. Finally, classification performance per dichotomizer results shows that the novel proposal is able to obtain similar or even better results while defining a more compact number of dichotomies and SVs compared to state-of-the-art approaches
Unbalanced load flow with hybrid wavelet transform and support vector machine based Error-Correcting Output Codes for power quality disturbances classification including wind energy
Purpose. The most common methods to designa multiclass classification consist to determine a set of binary classifiers and to combine them. In this paper support vector machine with Error-Correcting Output Codes (ECOC-SVM) classifier is proposed to classify and characterize the power qualitydisturbances such as harmonic distortion,voltage sag, and voltage swell include wind farms generator in power transmission systems. Firstly three phases unbalanced load flow analysis is executed to calculate difference electric network characteristics, levels of voltage, active and reactive power. After, discrete wavelet transform is combined with the probabilistic ECOC-SVM model to construct the classifier. Finally, the ECOC-SVM classifies and identifies the disturbance type according tothe energy deviation of the discrete wavelet transform. The proposedmethod gives satisfactory accuracy with 99.2% compared with well known methods and shows that each power quality disturbances has specific deviations from the pure sinusoidal waveform,this is good at recognizing and specifies the type of disturbance generated from the wind
power generator.Наиболее распространенные методы построения мультиклассовой классификации заключаются в определении набора двоичных классификаторов и их объединении. В данной статье предложена машина опорных векторов с классификатором выходных кодов исправления ошибок(ECOC-SVM) с целью классифицировать и характеризовать такие нарушения качества электроэнергии, как гармонические искажения, падение напряжения и скачок напряжения, включая генератор ветровых электростанций в системах передачи электроэнергии. Сначала выполняется анализ потока несимметричной нагрузки трех фаз для расчета разностных характеристик электрической сети, уровней напряжения, активной и реактивной мощности. После этого дискретное вейвлет-преобразование объединяется с вероятностной моделью ECOC-SVM для построения классификатора. Наконец, ECOC-SVM классифицирует и идентифицирует тип возмущения в соответствии с отклонением энергии дискретного вейвлет-преобразования. Предложенный метод дает удовлетворительную точность 99,2% по сравнению с хорошо известными методами и показывает, что каждое нарушение качества электроэнергии имеет определенные отклонения от чисто синусоидальной формы волны, что способствует распознаванию и определению типа возмущения, генерируемого ветровым генератором
Contextual Bag-Of-Visual-Words and ECOC-Rank for Retrieval and Multi-class Object Recognition
Projecte Final de Màster UPC realitzat en col.laboració amb Dept. Matemàtica Aplicada i Anàlisi, Universitat de BarcelonaMulti-class object categorization is an important line of research in Computer Vision
and Pattern Recognition fields. An artificial intelligent system is able to interact with its environment if it is able to distinguish among a set of cases, instances, situations, objects, etc. The World is inherently multi-class, and thus, the eficiency
of a system can be determined by its accuracy discriminating among a set of cases.
A recently applied procedure in the literature is the Bag-Of-Visual-Words (BOVW).
This methodology is based on the natural language processing theory, where a set of
sentences are defined based on word frequencies. Analogy, in the pattern recognition
domain, an object is described based on the frequency of its parts appearance.
However, a general drawback of this method is that the dictionary construction
does not take into account geometrical information about object parts. In order to
include parts relations in the BOVW model, we propose the Contextual BOVW
(C-BOVW), where the dictionary construction is guided by a geometricaly-based
merging procedure. As a result, objects are described as sentences where geometrical
information is implicitly considered.
In order to extend the proposed system to the multi-class case, we used the
Error-Correcting Output Codes framework (ECOC). State-of-the-art multi-class
techniques are frequently defined as an ensemble of binary classifiers. In this sense, the ECOC framework, based on error-correcting principles, showed to be a powerful tool, being able to classify a huge number of classes at the same time that corrects classification errors produced by the individual learners.
In our case, the C-BOVW sentences are learnt by means of an ECOC configuration, obtaining high discriminative power. Moreover, we used the ECOC outputs obtained by the new methodology to rank classes. In some situations, more than
one label is required to work with multiple hypothesis and find similar cases, such
as in the well-known retrieval problems. In this sense, we also included contextual
and semantic information to modify the ECOC outputs and defined an ECOC-rank methodology. Altering the ECOC output values by means of the adjacency of
classes based on features and classes relations based on ontologies, we also reporteda significant improvement in class-retrieval problems
Simultaneous class-modelling in chemometrics: A generalization of Partial Least Squares class modelling for more than two classes by using error correcting output code matrices
The paper presents a new methodology within the framework of the so-called compliant class-models, PLS2-CM,
designed with the purpose of improving the performance of class-modelling in a setting with more than two
classes. The improvement in the class-models is achieved through the use of multi-response PLS models with the
classes encoded via Error-Correcting Output Codes (ECOC), instead of the traditional class indicator variables
used in chemometrics.
The proposed PLS2-CM entails a decomposition of a class-modelling problem into a series of binary learners,
based on a family of code matrices with different code length, which are evaluated to obtain simultaneous
compliant class-models with the best performance.
The methodology develops both a new encoding system, based on multi-criteria optimization to search for
optimal coding matrices, and a new decoding system, based on probability thresholds to assign objects to classmodels. The whole procedure implies that the characteristics of the dataset at hand affect the final selection of the
coding matrix and therefore of built class-models, thus giving rise to a data-driven strategy.
The application of PLS2-CM to a variety of cases (controlled data, experimental data and repository datasets)
results in an enhanced class-modelling performance by means of the suggested procedure, as measured by the
DMCEN (Diagonal Modified Confusion Entropy) index and by sensitivity-specificity matrices. The predictive
ability of the compliant class-models has been evaluated.This work is part of the project with reference BU052P20 financed by Junta de Castilla y Leon, Conserjería de Educacion with the aid of European Regional Development Funds
Optimized One vs One approach in multiclass classification for early Alzheimer’s Disease and Mild Cognitive Impairment diagnosis
The detection of Alzheimer’s Disease in its early stages is crucial for patient care and drugs
development. Motivated by this fact, the neuroimaging community has extensively applied machine learning
techniques to the early diagnosis problem with promising results. The organization of challenges has helped
the community to address different raised problems and to standardize the approaches to the problem. In
this work we use the data from international challenge for automated prediction of MCI from MRI data
to address the multiclass classification problem. We propose a novel multiclass classification approach that
addresses the outlier detection problem, uses pairwise t-test feature selection, project the selected features
onto a Partial-Least-Squares multiclass subspace, and applies one-versus-one error correction output
codes classification. The proposed method yields to an accuracy of 67 % in the multiclass classification,
outperforming all the proposals of the competition.Ministerio de Innovacion y Ciencia Project DEEP-NEUROMAPS
RTI2018-098913-B100Consejeria de Economia, Innovacion, Ciencia, y Empleo of the Junta de Andalucia
A-TIC-080-UGR18 TIC FRONTERAGerman Research Foundation (DFG)
FPU 18/04902United States Department of Health & Human Services
National Institutes of Health (NIH) - USA
NIH National Institute of Neurological Disorders & Stroke (NINDS)
U01 AG024904DOD ADNI Department of Defense
W81XWH-12-2-001
Online error correcting output codes
a b s t r a c t This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem independent codings oneversus-all and one-versus-one is introduced. Furthermore, two new codings are proposed, unbalanced online ECOC and a problem dependent online ECOC. This last online coding technique takes advantage of the problem data for minimizing the number of dichotomizers used in the ECOC framework while preserving a high accuracy. These techniques are validated on an online setting of 11 data sets from UCI database and applied to two real machine vision applications: traffic sign recognition and face recognition. As a result, the online ECOC techniques proposed provide a feasible and robust way for handling new classes using any base classifier
Deep N-ary Error Correcting Output Codes
Ensemble learning consistently improves the performance of multi-class
classification through aggregating a series of base classifiers. To this end,
data-independent ensemble methods like Error Correcting Output Codes (ECOC)
attract increasing attention due to its easiness of implementation and
parallelization. Specifically, traditional ECOCs and its general extension
N-ary ECOC decompose the original multi-class classification problem into a
series of independent simpler classification subproblems. Unfortunately,
integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as
deep N-ary ECOC, is not straightforward and yet fully exploited in the
literature, due to the high expense of training base learners. To facilitate
the training of N-ary ECOC with deep learning base learners, we further propose
three different variants of parameter sharing architectures for deep N-ary
ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct
experiments by varying the backbone with different deep neural network
architectures for both image and text classification tasks. Furthermore,
extensive ablation studies on deep N-ary ECOC show its superior performance
over other deep data-independent ensemble methods.Comment: EAI MOBIMEDIA 202
Learning error-correcting representations for multi-class problems
[eng] Real life is full of multi-class decision tasks. In the Pattern Recognition field, several method- ologies have been proposed to deal with binary problems obtaining satisfying results in terms of performance. However, the extension of very powerful binary classifiers to the multi-class case is a complex task. The Error-Correcting Output Codes framework has demonstrated to be a very powerful tool to combine binary classifiers to tackle multi-class problems. However, most of the combinations of binary classifiers in the ECOC framework overlook the underlay- ing structure of the multi-class problem. In addition, is still unclear how the Error-Correction of an ECOC design is distributed among the different classes. In this dissertation, we are interested in tackling critic problems of the ECOC framework, such as the definition of the number of classifiers to tackle a multi-class problem, how to adapt the ECOC coding to multi-class data and how to distribute error-correction among different pairs of categories. In order to deal with this issues, this dissertation describes several proposals. 1) We define a new representation for ECOC coding matrices that expresses the pair-wise codeword separability and allows for a deeper understanding of how error-correction is distributed among classes. 2) We study the effect of using a logarithmic number of binary classifiers to treat the multi-class problem in order to obtain very efficient models. 3) In order to search for very compact ECOC coding matrices that take into account the distribution of multi-class data we use Genetic Algorithms that take into account the constraints of the ECOC framework. 4) We propose a discrete factorization algorithm that finds an ECOC configuration that allocates the error-correcting capabilities to those classes that are more prone to errors. The proposed methodologies are evaluated on different real and synthetic data sets: UCI Machine Learning Repository, handwriting symbols, traffic signs from a Mobile Mapping System, and Human Pose Recovery. The results of this thesis show that significant perfor- mance improvements are obtained on traditional coding ECOC designs when the proposed ECOC coding designs are taken into account. [[spa] En la vida cotidiana las tareas de decisión multi-clase surgen constantemente. En el campo de Reconocimiento de Patrones muchos métodos de clasificación binaria han sido propuestos obteniendo resultados altamente satisfactorios en términos de rendimiento. Sin embargo, la extensión de estos sofisticados clasificadores binarios al contexto multi-clase es una tarea compleja. En este ámbito, las estrategias de Códigos Correctores de Errores (CCEs) han demostrado ser una herramienta muy potente para tratar la combinación de clasificadores binarios. No obstante, la mayoría de arquitecturas de combinación de clasificadores binarios negligen la estructura del problema multi-clase. Sin embargo, el análisis de la distribución de corrección de errores entre clases es aún un problema abierto. En esta tesis doctoral, nos centramos en tratar problemas críticos de los códigos correctores de errores; la definición del número de clasificadores necesarios para tratar un problema multi-clase arbitrario; la adaptación de los problemas binarios al problema multi-clase y cómo distribuir la corrección de errores entre clases. Para dar respuesta a estas cuestiones, en esta tesis doctoral describimos varias propuestas. 1) Definimos una nueva representación para CCEs que expresa la separabilidad entre pares de códigos y nos permite una mejor comprensión de cómo se distribuye la corrección de errores entre distintas clases. 2) Estudiamos el efecto de usar un número logarítmico de clasificadores binarios para tratar el problema multi-clase con el objetivo de obtener modelos muy eficientes. 3) Con el objetivo de encontrar modelos muy eficientes que tienen en cuenta la estructura del problema multi-clase utilizamos algoritmos genéticos que tienen en cuenta las restricciones de los ECCs. 4) Pro- ponemos un algoritmo de factorización de matrices discreta que encuentra ECCs con una configuración que distribuye corrección de error a aquellas categorías que son más propensas a tener errores. Las metodologías propuestas son evaluadas en distintos problemas reales y sintéticos como por ejemplo: Repositorio UCI de Aprendizaje Automático, reconocimiento de símbolos escritos, clasificación de señales de tráfico y reconocimiento de la pose humana. Los resultados obtenidos en esta tesis muestran mejoras significativas en rendimiento comparados con los diseños tradiciones de ECCs cuando las distintas propuestas se tienen en cuenta
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