Utilizando Pesos estáticos e dinâmicos em sistemas multi-classificadores com diferentes níveis de diversidade

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

Sistemas orbitais e aéreos aplicados à análise multi-escala da tectônica rúptil atuante na borda sudeste da Bacia do Parnaíba

The tectonics activity on the southern border of Parnaíba Basin resulted in a wide range of brittle structures that affect siliciclastic sedimentary rocks. This tectonic activity and related faults, joints, and folds are poorly known. The main aims of this study were (1) to identify lineaments using several remotesensing systems, (2) to check how the interpretation based on these systems at several scales influence the identification of lineaments, and (3) to contribute to the knowledge of brittle tectonics in the southern border of the Parnaíba Basin. The integration of orbital and aerial systems allowed a multi-scale identification, classification, and quantification of lineaments. Maps of lineaments were elaborated in the following scales: 1:200,000 (SRTM Shuttle Radar Topographic Mission), 1:50,000 (Landsat 7 ETM+ satellite), 1:10,000 (aerial photographs) and 1:5,000 (Quickbird satellite). The classification of the features with structural significance allowed the determination of four structural sets: NW, NS, NE, and EW. They were usually identified in all remote-sensing systems. The NE-trending set was not easily identified in aerial photographs but was better visualized on images of medium-resolution systems (SRTM and Landsat 7 ETM+). The same behavior characterizes the NW-trending. The NS-and EW-trending sets were better identified on images from high-resolution systems (aerial photographs and Quickbird). The structural meaning of the lineaments was established after field work. The NEtrending set is associated with normal and strike-slip faults, including deformation bands. These are the oldest structures identified in the region and are related to the reactivation of Precambrian basement structures from the Transbrazilian Lineament. The NW-trending set represents strike-slip and subordinated normal faults. The high dispersion of this set suggests a more recent origin than the previous structures. The NW-trending set may be related to the Picos-Santa Inês Lineament. The NS-and EW-trending sets correspond to large joints (100 m 5 km long). The truncation relationships between these joint sets indicate that the EW-is older than the NS-trending set. The methodology developed by the present work is an excellent tool for the understanding of the regional and local tectonic structures in the Parnaíba basin. It helps the choice of the best remote-sensing system to identify brittle features in a poorly known sedimentary basin

Novas abordagens para configurações automáticas dos parâmetros de controle em comitês de classificadores

Significant advances have emerged in research related to the topic of Classifier Committees. The models that receive the most attention in the literature are those of the static nature, also known as ensembles. The algorithms that are part of this class, we highlight the methods that using techniques of resampling of the training data: Bagging, Boosting and Multiboosting. The choice of the architecture and base components to be recruited is not a trivial task and has motivated new proposals in an attempt to build such models automatically, and many of them are based on optimization methods. Many of these contributions have not shown satisfactory results when applied to more complex problems with different nature. In contrast, the thesis presented here, proposes three new hybrid approaches for automatic construction for ensembles: Increment of Diversity, Adaptive-fitness Function and Meta-learning for the development of systems for automatic configuration of parameters for models of ensemble. In the first one approach, we propose a solution that combines different diversity techniques in a single conceptual framework, in attempt to achieve higher levels of diversity in ensembles, and with it, the better the performance of such systems. In the second one approach, using a genetic algorithm for automatic design of ensembles. The contribution is to combine the techniques of filter and wrapper adaptively to evolve a better distribution of the feature space to be presented for the components of ensemble. Finally, the last one approach, which proposes new techniques for recommendation of architecture and based components on ensemble, by techniques of traditional meta-learning and multi-label meta-learning. In general, the results are encouraging and corroborate with the thesis that hybrid tools are a powerful solution in building effective ensembles for pattern classification problems.

AdaptMCloud: uma estratégia para adaptação dinâmica de aplicações Multi-Cloud

Multi-Cloud Applications are composed of services offered by multiple cloud platforms where the user/developer has full knowledge of the use of such platforms. The use of multiple cloud platforms avoids the following problems: (i) vendor lock-in, which is dependency on the application of a certain cloud platform, which is prejudicial in the case of degradation or failure of platform services, or even price increasing on service usage; (ii) degradation or failure of the application due to fluctuations in quality of service (QoS) provided by some cloud platform, or even due to a failure of any service. In multi-cloud scenario is possible to change a service in failure or with QoS problems for an equivalent of another cloud platform. So that an application can adopt the perspective multi-cloud is necessary to create mechanisms that are able to select which cloud services/platforms should be used in accordance with the requirements determined by the programmer/user. In this context, the major challenges in terms of development of such applications include questions such as: (i) the choice of which underlying services and cloud computing platforms should be used based on the defined user requirements in terms of functionality and quality (ii) the need to continually monitor the dynamic information (such as response time, availability, price, availability), related to cloud services, in addition to the wide variety of services, and (iii) the need to adapt the application if QoS violations affect user defined requirements. This PhD thesis proposes an approach for dynamic adaptation of multi-cloud applications to be applied when a service is unavailable or when the requirements set by the user/developer point out that other available multi-cloud configuration meets more efficiently. Thus, this work proposes a strategy composed of two phases. The first phase consists of the application modeling, exploring the similarities representation capacity and variability proposals in the context of the paradigm of Software Product Lines (SPL). In this phase it is used an extended feature model to specify the cloud service configuration to be used by the application (similarities) and the different possible providers for each service (variability). Furthermore, the non-functional requirements associated with cloud services are specified by properties in this model by describing dynamic information about these services. The second phase consists of an autonomic process based on MAPE-K control loop, which is responsible for selecting, optimally, a multicloud configuration that meets the established requirements, and perform the adaptation. The adaptation strategy proposed is independent of the used programming technique for performing the adaptation. In this work we implement the adaptation strategy using various programming techniques such as aspect-oriented programming, context-oriented programming and components and services oriented programming. Based on the proposed steps, we tried to assess the following: (i) the process of modeling and the specification of non-functional requirements can ensure effective monitoring of user satisfaction; (ii) if the optimal selection process presents significant gains compared to sequential approach; and (iii) which techniques have the best trade-off when compared efforts to development/modularity and performance.