Made available in DSpace on 2014-12-17T15:47:48Z (GMT). No. of bitstreams: 1 ValeriaMSB.pdf: 564848 bytes, checksum: fbed1b62b5d33ac05db3c528f1bdcf62 (MD5) Previous issue date: 2006-07-14 / The use of intelligent agents in multi-classifier systems appeared in order to making the centralized decision process of a multi-classifier system into a distributed, flexible and incremental one. Based on this, the NeurAge (Neural Agents) system (Abreu et al 2004) was proposed. This system has a superior performance to some combination-centered methods (Abreu, Canuto, and Santana 2005). The negotiation is important to the multiagent system performance, but most of negotiations are defined informaly. A way to formalize the negotiation process is using an ontology. In the context of classification tasks, the ontology provides an approach to formalize the concepts and rules that manage the relations between these concepts. This work aims at using ontologies to make a formal description of the negotiation methods of a multi-agent system for classification tasks, more specifically the NeurAge system. Through ontologies, we intend to make the NeurAge system more formal and open, allowing that new agents can be part of such system during the negotiation. In this sense, the NeurAge System will be studied on the basis of its functioning and reaching, mainly, the negotiation methods used by the same ones. After that, some negotiation ontologies found in literature will be studied, and then those that were chosen for this work will be adapted to the negotiation methods used in the NeurAge. / A utiliza??o de agentes inteligentes em sistemas multi-classificadores surgiu devido ? necessidade de tornar o processo de tomada de decis?o de tais sistemas distribu?do, aut?nomo e flex?vel. Baseado nisso, foi proposto o sistema NeurAge (Neural Agents) (Abreu et al 2004). Este sistema possui um desempenho superior a v?rios m?todos de combina??o centralizados (Abreu, Canuto, and Santana 2005). A negocia??o ? importante para o desempenho de um sistema multiagente, por?m a maioria das negocia??es s?o definidas de maneira informal. Um modo de formalizar as negocia??es ? atrav?s do uso de ontologias. Dentro do contexto de classifica??o de padr?es, o uso de ontologias fornece uma abordagem para formalizar os conceitos e regras que governam as rela??es entre esses conceitos. O objetivo deste trabalho ? utilizar ontologias para formalizar os m?todos de negocia??o de um sistema multiagente para reconhecimento de padr?es, mais especificamente o sistema NeurAge. Atrav?s de ontologias, pretende-se deixar o sistema NeurAge mais formal e aberto, permitindo que novos agentes possam fazer parte de tal sistema durante o processo de negocia??o. Para a realiza??o deste objetivo, o Sistema NeurAge ser? estudado com base em seu funcionamento e focalizando, principalmente, os m?todos de negocia??o utilizados pelo mesmo. Na seq??ncia, algumas ontologias para negocia??o encontradas na literatura ser?o estudadas, e ent?o aquelas que foram escolhidas para este trabalho ser?o adaptadas aos m?todos de negocia??o utilizados no NeurAge.

Ferramentas de geoinformação possuem grande aplicabilidade na compreensão e no mapeamento de deslizamentos. Considerando-se a importância dos componentes do relevo e da cobertura do solo neste processo, torna-se essencial o estabelecimento de metodologias para a síntese de informações do relevo e para a identificação de cicatrizes de deslizamento, de maneira a facilitar o monitoramento de áreas de risco. O objetivo desta Tese é propor metodologias de processamento digital de imagens para o mapeamento e identificação de cicatrizes de deslizamento próximo a rodovias. Um deslizamento de grande porte com várias consequências econômicas, ocorrido no ano de 1999, às margens da Rodovia Anchieta, na bacia hidrográfica do Rio Pilões foi utilizado como área de estudo deste trabalho. Utilizando dados gratuitos, mapas de cobertura do solo e de compartimentação do relevo foram gerados e analisados conjuntamente para a identificação das áreas de potenciais cicatrizes na região das Rodovias Anchieta e Imigrantes. A análise do relevo foi realizada utilizando técnicas de classificação baseada em objeto. A identificação de áreas de cicatrizes de deslizamento foi realizada através da avaliação de duas estratégias metodológicas: uma utilizando o algoritmo de classificação supervisionada SVM (Support Vector Machine) aplicado ao índice de vegetação NDVI (Normalized Difference Vegetation Index) e outra que utilizando combinação entre diferentes classificadores para a composição de uma classificação final. Os resultados obtidos para o mapeamento do relevo mostraram que a metodologia proposta possui grande potencial para a descrição de feições do relevo, com maior nível de detalhamento, facilitando a identificação de áreas com grande potencial de ocorrência de deslizamentos. Ambas as metodologias de identificação de cicatrizes de deslizamento apresentaram bons resultados, sendo que a combinação entre os algoritmos SVM, Redes Neurais e Máxima Verossimilhança apresentou o resultado mais adequado com os objetivos do trabalho, atingindo erro de omissão inferior a 10% para a classe de deslizamento. A combinação dos dois produtos permitiu a análise e identificação de diversas áreas de potenciais cicatrizes de deslizamento associadas à rodovias na região de estudo. A metodologia proposta possui ampla replicabilidade, podendo ser utilizada para análises de risco associadas a assentamentos urbanos, empreendimentos lineares e para o planejamento territorial e ambiental. / Geoinformation tools have great applicability in understanding and mapping landslides. Considering the significance of releif components and land cover in this process, it is essential the establishment of methods for the synthesis of the relief information and identification landslides, aiming to facilitate areas risk monitoring. The objective of this Dissertation is to propose digital image processing methodologies for map and identify landslide near to highways. A large landslide with several economic consequences was used as a study area of this work, occurred in 1999, near the Highway Anchieta, in Piloes river basin. Using free data, land cover and relief subdivsion maps were generated and intersected to identify areas of potential landslides in the region of Highways Anchieta and Imigrantes. The relief analysis was performed using based on object classification techniques. The identification of the landslide was performed by evaluating two methodological strategies: one using the supervised classification algorithm SVM (Support Vector Machine) applied to the NDVI vegetation index (Normalized Difference Vegetation Index) and another using combination of different classifiers for the composition of a final classification. The results obtained for relief mapping showed that the proposed method has great potential for the description of the relief features, with greater detail, facilitating the identification of areas with high potential for occurrence of landslides. Both landslides identification methodologies showed good results, and the combination of SVM, Neural Network and Maximum Likelihood algorithms presented the most appropriate result, reaching omission error of less than 10% for the landslide class. The combination of the two products allowed the analysis and identification of several areas of potential landslide scars associated with roads in the study area. The proposed methodology has extensive replication and can be used for risk analysis associated with urban settlements, linear infrastructures and the territorial and environmental planning.

In a wide range of applications, decisions must be made by combining information from multiple agents with varying levels of trust and expertise. For example, citizen science involves large numbers of human volunteers with differing skills, while disaster management requires aggregating information from multiple people and devices to make timely decisions. This thesis introduces efficient and scalable Bayesian inference for decision combination, allowing us to fuse the responses of multiple agents in large, real-world problems and account for the agents’ unreliability in a principled manner. As the behaviour of individual agents can change significantly, for example if agents move in a physical space or learn to perform an analysis task, this work proposes a novel combination method that accounts for these time variations in a fully Bayesian manner using a dynamic generalised linear model. This approach can also be used to augment agents’ responses with continuous feature data, thus permitting decision-making when agents’ responses are in limited supply. Working with information inferred using the proposed Bayesian techniques, an information-theoretic approach is developed for choosing optimal pairs of tasks and agents. This approach is demonstrated by an algorithm that maintains a trustworthy pool of workers and enables efficient learning by selecting informative tasks. The novel methods developed here are compared theoretically and empirically to a range of existing decision combination methods, using both simulated and real data. The results show that the methodology proposed in this thesis improves accuracy and computational efficiency over alternative approaches, and allows for insights to be determined into the behavioural groupings of agents.

Made available in DSpace on 2014-12-17T15:47:44Z (GMT). No. of bitstreams: 1 RaulBP.pdf: 1811907 bytes, checksum: 007d54350318472b95b8e06144b749a5 (MD5) Previous issue date: 2007-07-27 / Although some individual techniques of supervised Machine Learning (ML), also known as classifiers, or algorithms of classification, to supply solutions that, most of the time, are considered efficient, have experimental results gotten with the use of large sets of pattern and/or that they have a expressive amount of irrelevant data or incomplete characteristic, that show a decrease in the efficiency of the precision of these techniques. In other words, such techniques can t do an recognition of patterns of an efficient form in complex problems. With the intention to get better performance and efficiency of these ML techniques, were thought about the idea to using some types of LM algorithms work jointly, thus origin to the term Multi-Classifier System (MCS). The MCS s presents, as component, different of LM algorithms, called of base classifiers, and realized a combination of results gotten for these algorithms to reach the final result. So that the MCS has a better performance that the base classifiers, the results gotten for each base classifier must present an certain diversity, in other words, a difference between the results gotten for each classifier that compose the system. It can be said that it does not make signification to have MCS s whose base classifiers have identical answers to the sames patterns. Although the MCS s present better results that the individually systems, has always the search to improve the results gotten for this type of system. Aim at this improvement and a better consistency in the results, as well as a larger diversity of the classifiers of a MCS, comes being recently searched methodologies that present as characteristic the use of weights, or confidence values. These weights can describe the importance that certain classifier supplied when associating with each pattern to a determined class. These weights still are used, in associate with the exits of the classifiers, during the process of recognition (use) of the MCS s. Exist different ways of calculating these weights and can be divided in two categories: the static weights and the dynamic weights. The first category of weights is characterizes for not having the modification of its values during the classification process, different it occurs with the second category, where the values suffers modifications during the classification process. In this work an analysis will be made to verify if the use of the weights, statics as much as dynamics, they can increase the perfomance of the MCS s in comparison with the individually systems. Moreover, will be made an analysis in the diversity gotten for the MCS s, for this mode verify if it has some relation between the use of the weights in the MCS s with different levels of diversity / Apesar de algumas t?cnicas individuais de Aprendizado de M?quina (AM) supervisionado, tamb?mconhecidos como classificadores, ou algoritmos de classifica??o, fornecerem solu??es que, na maioria das vezes, s?o consideradas eficientes, h? resultados experimentais obtidos com a utiliza??o de grandes conjuntos de padr?es e/ou que apresentam uma quantidade expressiva de dados incompletos ou caracter?sticas irrelevantes, que mostram uma queda na efic?cia da precis?o dessas t?cnicas. Ou seja, tais t?cnicas n?o conseguem realizar um reconhecimento de padr?es de uma forma eficiente em problemas complexos. Com o intuito de obter um melhor desempenho e efic?cia dessas t?cnicas de AM, pensouse na id?ia de fazer com que v?rios tipos de algoritmos de AM consigam trabalhar conjuntamente, dando assim origem ao termo Sistema Multi-Classificador (SMC). Os SMC s apresentam, como componentes, diferentes algoritmos de AM, chamados de classificadores base, e realizam uma combina??o dos resultados obtidos por estes algoritmos para atingir o resultado final. Para que o SMC tenha um desempenho melhor que os classificadores base, os resultados obtidos por cada classificador base devem apresentar uma determinada diversidade, ou seja, uma diferen?a entre os resultados obtidos por cada classificador que comp?em o sistema. Pode-se dizer que n?o faz sentido ter SMC s cujos classificadores base possuam respostas id?nticas aos padr?es apresentados. Apesar dos SMC s apresentarem melhores resultados que os sistemas executados individualmente, h? sempre a busca para melhorar os resultados obtidos por esse tipo de sistema. Visando essa melhora e uma maior consist?ncia nos resultados, assim como uma maior diversidade dos classificadores de um SMC, v?m sendo recentemente pesquisadas metodologias que apresentam como caracter?sticas o uso de pesos, ou valores de con- fian?a. Esses pesos podem descrever a import?ncia que um determinado classificador forneceu ao associar cada padr?o a uma determinada classe. Esses pesos ainda s?o utilizados, em conjunto com as sa?das dos classificadores, durante o processo de reconhecimento (uso) dos SMC s. Existem diferentes maneiras de se calcular esses pesos e podem ser divididas em duas categorias: os pesos est?ticos e os pesos din?micos. A primeira categoria de pesos se caracteriza por n?o haver a modifica??o de seus valores no decorrer do processo de classifica??o, ao contr?rio do que ocorre com a segunda categoria, onde os valores sofrem modifica??es no decorrer do processo de classifica??o. Neste trabalho ser? feito uma an?lise para verificar se o uso dos pesos, tanto est?ticos quanto din?micos, conseguem aumentar o desempenho dos SMC s em compara??o com estes sistemas executados individualmente. Al?m disso, ser? feita uma an?lise na diversidade obtida pelos SMC s, para dessa forma verificar se h? alguma rela??o entre o uso dos pesos nos SMC s com diferentes n?veis de diversidade

Dans cette thèse, nous proposons plusieurs nouvelles techniques pour la classification d'images hyperspectrales basées sur l'apprentissage d'ensemble. Le cadre proposé introduit des innovations importantes par rapport aux approches précédentes dans le même domaine, dont beaucoup sont basées principalement sur un algorithme individuel. Tout d'abord, nous proposons d'utiliser la Forêt de Rotation (Rotation Forest) avec différentes techiniques d'extraction de caractéristiques linéaire et nous comparons nos méthodes avec les approches d'ensemble traditionnelles, tels que Bagging, Boosting, Sous-espace Aléatoire et Forêts Aléatoires. Ensuite, l'intégration des machines à vecteurs de support (SVM) avec le cadre de sous-espace de rotation pour la classification de contexte est étudiée. SVM et sous-espace de rotation sont deux outils puissants pour la classification des données de grande dimension. C'est pourquoi, la combinaison de ces deux méthodes peut améliorer les performances de classification. Puis, nous étendons le travail de la Forêt de Rotation en intégrant la technique d'extraction de caractéristiques locales et l'information contextuelle spatiale avec un champ de Markov aléatoire (MRF) pour concevoir des méthodes spatio-spectrale robustes. Enfin, nous présentons un nouveau cadre général, ensemble de sous-espace aléatoire, pour former une série de classifieurs efficaces, y compris les arbres de décision et la machine d'apprentissage extrême (ELM), avec des profils multi-attributs étendus (EMaPS) pour la classification des données hyperspectrales. Six méthodes d'ensemble de sous-espace aléatoire, y compris les sous-espaces aléatoires avec les arbres de décision, Forêts Aléatoires (RF), la Forêt de Rotation (RoF), la Forêt de Rotation Aléatoires (Rorf), RS avec ELM (RSELM) et sous-espace de rotation avec ELM (RoELM), sont construits par multiples apprenants de base. L'efficacité des techniques proposées est illustrée par la comparaison avec des méthodes de l'état de l'art en utilisant des données hyperspectrales réelles dans de contextes différents. / In this thesis, we propose several new techniques for the classification of hyperspectral remote sensing images based on multiple classifier system (MCS). Our proposed framework introduces significant innovations with regards to previous approaches in the same field, many of which are mainly based on an individual algorithm. First, we propose to use Rotation Forests with several linear feature extraction and compared them with the traditional ensemble approaches, such as Bagging, Boosting, Random subspace and Random Forest. Second, the integration of the support vector machines (SVM) with Rotation subspace framework for context classification is investigated. SVM and Rotation subspace are two powerful tools for high-dimensional data classification. Therefore, combining them can further improve the classification performance. Third, we extend the work of Rotation Forests by incorporating local feature extraction technique and spatial contextual information with Markov random Field (MRF) to design robust spatial-spectral methods. Finally, we presented a new general framework, Random subspace ensemble, to train series of effective classifiers, including decision trees and extreme learning machine (ELM), with extended multi-attribute profiles (EMAPs) for classifying hyperspectral data. Six RS ensemble methods, including Random subspace with DT (RSDT), Random Forest (RF), Rotation Forest (RoF), Rotation Random Forest (RoRF), RS with ELM (RSELM) and Rotation subspace with ELM (RoELM), are constructed by the multiple base learners. The effectiveness of the proposed techniques is illustrated by comparing with state-of-the-art methods by using real hyperspectral data sets with different contexts.

Submitted by João Arthur Martins (joao.arthur@ufpe.br) on 2015-03-12T17:38:41Z No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T14:23:38Z (GMT) No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-13T14:23:38Z (GMT). No. of bitstreams: 2 Dissertaçao Tiago de Lima.pdf: 1469834 bytes, checksum: 95a0326778b3d0f98bd35a7449d8b92f (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2013-02-26 / In this dissertation, we present a methodology that aims the automatic construction of multi-classifiers systems based on the combination of selection and fusion. The presented method initially finds an optimum number of clusters for training data set and subsequently determines an ensemble for each cluster found. For model evaluation, the testing data set are submitted to clustering techniques and the nearest cluster to data input will emit a supervised response through its associated ensemble. Self-organizing maps were used in the clustering phase and multilayer perceptrons were used in the classification phase. Adaptive differential evolution has been used in this work in order to optimize the parameters and performance of the different techniques used in the classification and clustering phases. The proposed method, called SFJADE - Selection and Fusion (SF) via Adaptive Differential Evolution (JADE), has been tested on data compression of signals generated by artificial nose sensors and well-known classification problems, including cancer, card, diabetes, glass, heart, horse, soybean and thyroid. The experimental results have shown that the SFJADE method has a better performance than some literature methods while significantly outperforming most of the methods commonly used to construct Multi-Classifier Systems. / Nesta dissertação, nós apresentamos uma metodologia que almeja a construção automática de sistemas de múltiplos classificadores baseados em uma combinação de seleção e fusão. O método apresentado inicialmente encontra um número ótimo de grupos a partir do conjunto de treinamento e subsequentemente determina um comitê para cada grupo encontrado. Para avaliação do modelo, os dados de teste são submetidos à técnica de agrupamento e o grupo mais próximo do dado de entrada irá emitir uma resposta supervisionada por meio de seu comitê associado. Mapas Auto Organizáveis foi usado na fase de agrupamento e Perceptrons de múltiplas camadas na fase de classificação. Evolução Diferencial Adaptativa foi utilizada neste trabalho a fim de otimizar os parâmetros e desempenho das diferentes técnicas utilizadas nas fases de classificação e agrupamento de dados. O método proposto, chamado SFJADE – Selection and Fusion (SF) via Adaptive Differential Evolution (JADE), foi testado em dados gerados para sensores de um nariz artificial e problemas de referência em classificação de padrões, que são: cancer, card, diabetes, glass, heart, heartc e horse. Os resultados experimentais mostraram que SFJADE possui um melhor desempenho que alguns métodos da literatura, além de superar a maioria dos métodos geralmente usados para a construção de sistemas de múltiplos classificadores.

Durant l'última dècada, els sistemes classificadors (LCS) d'estil Michigan - sistemes d'aprenentatge automàtic que combinen tècniques de repartiment de crèdit i algorismes genètics (AG) per evolucionar una població de classificadors online- han renascut. Juntament amb la formulació dels sistemes de primera generació, s'han produït avenços importants en (1) el disseny sistemàtic de nous LCS competents, (2) la seva aplicació en dominis rellevants i (3) el desenvolupament d'anàlisis teòriques. Malgrat aquests dissenys i aplicacions importants, encara hi ha reptes complexos que cal abordar per comprendre millor el funcionament dels LCS i per solucionar problemes del món real eficientment i escalable.Aquesta tesi tracta dos reptes importants - compartits amb la comunitat d'aprenentatge automàtic - amb LCS d'estil Michigan: (1) aprenentatge en dominis que contenen classes estranyes i (2) evolució de models comprensibles on s'utilitzin mètodes de raonament similars als humans. L'aprenentatge de models precisos de classes estranyes és crític, doncs el coneixement clau sol quedar amagat en exemples d'aquestes, i la majoria de tècniques d'aprenentatge no són capaces de modelar la raresa amb precisió. La detecció de rareses sol ser complicat en aprenentatge online ja que el sistema d'aprenentatge rep un flux d'exemples i ha de detectar les rareses al vol. D'altra banda, l'evolució de models comprensibles és crucial en certs dominis com el mèdic, on l'expert acostuma a estar més interessat en obtenir una explicació intel·ligible de la predicció que en la predicció en si mateixa.El treball present considera dos LCS d'estil Michigan com a punt de partida: l'XCS i l 'UCS. Es pren l'XCS com a primera referència ja que és l'LCS que ha tingut més influencia fins al moment. L'UCS hereta els components principals de l'XCS i els especialitza per aprenentatge supervisat. Tenint en compte que aquesta tesi especialment se centra en problemes de classificació, l'UCS també es considera en aquest estudi. La inclusió de l'UCS marca el primer objectiu de la tesi, sota el qual es revisen un conjunt de punts que van restar oberts en el disseny del sistema. A més, per il·lustrar les diferències claus entre l'XCS i l'UCS, es comparen ambdós sistemes sobre una bateria de problemes artificials de complexitat acotada.L'estudi de com els LCS aprenen en dominis amb classes estranyes comença amb un estudi analític que descompon el problema en cinc elements crítics i deriva models per facetes per cadascun d'ells. Aquesta anàlisi s'usa com a eina per dissenyar guies de configuració que permeten que l'XCS i l'UCS solucionin problemes que prèviament no eren resolubles. A continuació, es comparen els dos LCS amb alguns dels sistemes d'aprenentatge amb més influencia en la comunitat d'aprenentatge automàtic sobre una col·lecció de problemes del món real que contenen classes estranyes. Els resultats indiquen que els dos LCS són els mètodes més robustos de la comparativa. Així mateix, es demostra experimentalment que remostrejar els conjunts d'entrenament amb l'objectiu d'eliminar la presencia de classes estranyes beneficia, en mitjana, el rendiment de les tècniques d'aprenentatge.El repte de crear models més comprensibles i d'usar mecanismes de raonament que siguin similars als humans s'aborda mitjançant el disseny d'un nou LCS per aprenentatge supervisat que combina les capacitats d'avaluació de regles online, la robustesa mostrada pels AG en problemes complexos i la representació comprensible i mètodes de raonament fonamentats proporcionats per la lògica difusa. El nou LCS, anomenat Fuzzy-UCS, s'estudia en detall i es compara amb una bateria de mètodes d'aprenentatge. Els resultats de la comparativa demostren la competitivitat del Fuzzy-UCS en termes de precisió i intel·ligibilitat dels models evolucionats. Addicionalment, s'usa Fuzzy-UCS per extreure models de classificació acurats de grans volums de dades, exemplificant els avantatges de l'arquitectura d'aprenentatge online del Fuzzy-UCS.En general, les observacions i avenços assolits en aquesta tesi contribueixen a augmentar la comprensió del funcionament dels LCS i en preparar aquests tipus de sistemes per afrontar problemes del món real de gran complexitat. Finalment, els resultats experimentals ressalten la robustesa i competitivitat dels LCS respecte a altres mètodes d'aprenentatge, encoratjant el seu ús per tractar nous problemes del món real. / Durante la última década, los sistemas clasificadores (LCS) de estilo Michigan - sistemas de aprendizaje automático que combinan técnicas de repartición de crédito y algoritmos genéticos (AG) para evolucionar una población de clasificadores online - han renacido. Juntamente con la formulación de los sistemas de primera generación, se han producido avances importantes en (1) el diseño sistemático de nuevos LCS competentes, (2) su aplicación en dominios relevantes y (3) el desarrollo de análisis teóricos. Pese a eso, aún existen retos complejos que deben ser abordados para comprender mejor el funcionamiento de los LCS y para solucionar problemas del mundo real escalable y eficientemente.Esta tesis trata dos retos importantes - compartidos por la comunidad de aprendizaje automático - con LCS de estilo Michigan: (1) aprendizaje en dominios con clases raras y (2) evolución de modelos comprensibles donde se utilicen métodos de razonamiento similares a los humanos. El aprendizaje de modelos precisos de clases raras es crítico pues el conocimiento clave suele estar escondido en ejemplos de estas clases, y la mayoría de técnicas de aprendizaje no son capaces de modelar la rareza con precisión. El modelado de las rarezas acostumbra a ser más complejo en entornos de aprendizaje online, pues el sistema de aprendizaje recibe un flujo de ejemplos y debe detectar las rarezas al vuelo. La evolución de modelos comprensibles es crucial en ciertos dominios como el médico, donde el experto está más interesado en obtener una explicación inteligible de la predicción que en la predicción en sí misma.El trabajo presente considera dos LCS de estilo Michigan como punto de partida: el XCS y el UCS. Se toma XCS como primera referencia debido a que es el LCS que ha tenido más influencia hasta el momento. UCS es un diseño reciente de LCS que hereda los componentes principales de XCS y los especializa para aprendizaje supervisado. Dado que esta tesis está especialmente centrada en problemas de clasificación automática, también se considera UCS en el estudio. La inclusión de UCS marca el primer objetivo de la tesis, bajo el cual se revisan un conjunto de aspectos que quedaron abiertos durante el diseño del sistema. Además, para ilustrar las diferencias claves entre XCS y UCS, se comparan ambos sistemas sobre una batería de problemas artificiales de complejidad acotada.El estudio de cómo los LCS aprenden en dominios con clases raras empieza con un estudio analítico que descompone el problema en cinco elementos críticos y deriva modelos por facetas para cada uno de ellos. Este análisis se usa como herramienta para diseñar guías de configuración que permiten que XCS y UCS solucionen problemas que previamente no eran resolubles. A continuación, se comparan los dos LCS con algunos de los sistemas de aprendizaje de mayor influencia en la comunidad de aprendizaje automático sobre una colección de problemas del mundo real que contienen clases raras.Los resultados indican que los dos LCS son los métodos más robustos de la comparativa. Además, se demuestra experimentalmente que remuestrear los conjuntos de entrenamiento con el objetivo de eliminar la presencia de clases raras beneficia, en promedio, el rendimiento de los métodos de aprendizaje automático incluidos en la comparativa.El reto de crear modelos más comprensibles y usar mecanismos de razonamiento que sean similares a los humanos se aborda mediante el diseño de un nuevo LCS para aprendizaje supervisado que combina las capacidades de evaluación de reglas online, la robustez mostrada por los AG en problemas complejos y la representación comprensible y métodos de razonamiento proporcionados por la lógica difusa. El sistema que resulta de la combinación de estas ideas, llamado Fuzzy-UCS, se estudia en detalle y se compara con una batería de métodos de aprendizaje altamente reconocidos en el campo de aprendizaje automático. Los resultados de la comparativa demuestran la competitividad de Fuzzy-UCS en referencia a la precisión e inteligibilidad de los modelos evolucionados. Adicionalmente, se usa Fuzzy-UCS para extraer modelos de clasificación precisos de grandes volúmenes de datos, ejemplificando las ventajas de la arquitectura de aprendizaje online de Fuzzy-UCS.En general, los avances y observaciones proporcionados en la tesis presente contribuyen a aumentar la comprensión del funcionamiento de los LCS y a preparar estos tipos de sistemas para afrontar problemas del mundo real de gran complejidad. Además, los resultados experimentales resaltan la robustez y competitividad de los LCS respecto a otros métodos de aprendizaje, alentando su uso para tratar nuevos problemas del mundo real. / During the last decade, Michigan-style learning classifier systems (LCSs) - genetic-based machine learning (GBML) methods that combine apportionment of credit techniques and genetic algorithms (GAs) to evolve a population of classifiers online - have been enjoying a renaissance. Together with the formulation of first generation systems, there have been crucial advances in (1) systematic design of new competent LCSs, (2) applications in important domains, and (3) theoretical analyses for design. Despite these successful designs and applications, there still remain difficult challenges that need to be addressed to increase our comprehension of how LCSs behave and to scalably and efficiently solve real-world problems.The purpose of this thesis is to address two important challenges - shared by the machine learning community - with Michigan-style LCSs: (1) learning from domains that contain rare classes and (2) evolving highly legible models in which human-like reasoning mechanisms are employed. Extracting accurate models from rare classes is critical since the key, unperceptive knowledge usually resides in the rarities, and many traditional learning techniques are not able to model rarity accurately. Besides, these difficulties are increased in online learning, where the learner receives a stream of examples and has to detect rare classes on the fly. Evolving highly legible models is crucial in some domains such as medical diagnosis, in which human experts may be more interested in the explanation of the prediction than in the prediction itself.The contributions of this thesis take two Michigan-style LCSs as starting point: the extended classifier system (XCS) and the supervised classifier system (UCS). XCS is taken as the first reference of this work since it is the most influential LCS. UCS is a recent LCS design that has inherited the main components of XCS and has specialized them for supervised learning. As this thesis is especially concerned with classification problems, UCS is also considered in this study. Since UCS is still a young system, for which there are several open issues that need further investigation, its learning architecture is first revised and updated. Moreover, to illustrate the key differences between XCS and UCS, the behavior of both systems is compared % and show that UCS converges quickly than XCS on a collection of boundedly difficult problems.The study of learning from rare classes with LCSs starts with an analytical approach in which the problem is decomposed in five critical elements, and facetwise models are derived for each element. The analysis is used as a tool for designing configuration guidelines that enable XCS and UCS to solve problems that previously eluded solution. Thereafter, the two LCSs are compared with several highly-influential learners on a collection of real-world problems with rare classes, appearing as the two best techniques of the comparison. Moreover, re-sampling the training data set to eliminate the presence of rare classes is demonstrated to benefit, on average, the performance of LCSs.The challenge of building more legible models and using human-like reasoning mechanisms is addressed with the design of a new LCS for supervised learning that combines the online evaluation capabilities of LCSs, the search robustness over complex spaces of GAs, and the legible knowledge representation and principled reasoning mechanisms of fuzzy logic. The system resulting from this crossbreeding of ideas, referred to as Fuzzy-UCS, is studied in detail and compared with several highly competent learning systems, demonstrating the competitiveness of the new architecture in terms of the accuracy and the interpretability of the evolved models. In addition, the benefits provided by the online architecture are exemplified by extracting accurate classification models from large data sets.Overall, the advances and key insights provided in this thesis help advance our understanding of how LCSs work and prepare these types of systems to face increasingly difficult problems, which abound in current industrial and scientific applications. Furthermore, experimental results highlight the robustness and competitiveness of LCSs with respect to other machine learning techniques, which encourages their use to face new challenging real-world applications.