2024-03-29T16:01:22Zhttps://www.tdx.cat/oai/requestoai:www.tdx.cat:10803/3961242017-08-31T22:30:01Zcom_10803_1col_10803_84
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Codis de correcció d'errors (Teoria de la informació)
Códigos correctores de errores (Teoría de la información)
Error-correcting codes (Information theory)
Presa de decisions multicriteri
Toma de decisiones multicriterio
Multiple criteria decision making
Sistemes classificadors (Intel·ligència artificial)
Sistemas clasificadores
Learning classifier systems
Algorismes genètics
Algoritmos genéticos
Genetic algorithms
Matrius (Matemàtica)
Matrices (Matemáticas)
Matrices
Learning error-correcting representations for multi-class problems
[Barcelona] :
Universitat de Barcelona,
2016
Accés lliure
http://hdl.handle.net/10803/396124
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Bautista Martín, Miguel Ángel,
autor
1 recurs en línia (138 pàgines)
Tesi
Doctorat
Universitat de Barcelona. Departament de Matemàtica Aplicada i Anàlisi
2016
Universitat de Barcelona. Departament de Matemàtica Aplicada i Anàlisi
Tesis i dissertacions electròniques
Escalera Guerro, Sergio,
supervisor acadèmic
Pujol Vila, Oriol,
supervisor acadèmic
TDX
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.
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