Algoritmos genéticos aplicados a um comitê de LS-SVM em problemas de classificação

The pattern classification is one of the machine learning subareas that has the most outstanding. Among the various approaches to solve pattern classification problems, the Support Vector Machines (SVM) receive great emphasis, due to its ease of use and good generalization performance. The Least Squares formulation of SVM (LS-SVM) finds the solution by solving a set of linear equations instead of quadratic programming implemented in SVM. The LS-SVMs provide some free parameters that have to be correctly chosen to achieve satisfactory results in a given task. Despite the LS-SVMs having high performance, lots of tools have been developed to improve them, mainly the development of new classifying methods and the employment of ensembles, in other words, a combination of several classifiers. In this work, our proposal is to use an ensemble and a Genetic Algorithm (GA), search algorithm based on the evolution of species, to enhance the LSSVM classification. In the construction of this ensemble, we use a random selection of attributes of the original problem, which it splits the original problem into smaller ones where each classifier will act. So, we apply a genetic algorithm to find effective values of the LS-SVM parameters and also to find a weight vector, measuring the importance of each machine in the final classification. Finally, the final classification is obtained by a linear combination of the decision values of the LS-SVMs with the weight vector. We used several classification problems, taken as benchmarks to evaluate the performance of the algorithm and compared the results with other classifiers

Medição da densidade óssea através de ondas eletromagnéticas e classificação por MVS

This work holds the purpose of presenting an auxiliary way of bone density measurement through the attenuation of electromagnetic waves. In order to do so, an arrangement of two microstrip antennas with rectangular configuration has been used, operating in a frequency of 2,49 GHz, and fed by a microstrip line on a substrate of fiberglass with permissiveness of 4.4 and height of 0,9 cm. Simulations were done with silica, bone meal, silica and gypsum blocks samples to prove the variation on the attenuation level of different combinations. Because of their good reproduction of the human beings anomaly aspects, samples of bovine bone were used. They were subjected to weighing, measurement and microwave radiation. The samples had their masses altered after mischaracterization and the process was repeated. The obtained data were inserted in a neural network and its training was proceeded with the best results gathered by correct classification on 100% of the samples. It comes to the conclusion that through only one non-ionizing wave in the 2,49 GHz zone it is possible to evaluate the attenuation level in the bone tissue, and that with the appliance of neural network fed with obtained characteristics in the experiment it is possible to classify a sample as having low or high bone density

Arquiteturas eficientes para sensoriamento espectral e classificação automática de modulações usando características cicloestacionárias

The increasing demand for high performance wireless communication systems has shown the inefficiency of the current model of fixed allocation of the radio spectrum. In this context, cognitive radio appears as a more efficient alternative, by providing opportunistic spectrum access, with the maximum bandwidth possible. To ensure these requirements, it is necessary that the transmitter identify opportunities for transmission and the receiver recognizes the parameters defined for the communication signal. The techniques that use cyclostationary analysis can be applied to problems in either spectrum sensing and modulation classification, even in low signal-to-noise ratio (SNR) environments. However, despite the robustness, one of the main disadvantages of cyclostationarity is the high computational cost for calculating its functions. This work proposes efficient architectures for obtaining cyclostationary features to be employed in either spectrum sensing and automatic modulation classification (AMC). In the context of spectrum sensing, a parallelized algorithm for extracting cyclostationary features of communication signals is presented. The performance of this features extractor parallelization is evaluated by speedup and parallel eficiency metrics. The architecture for spectrum sensing is analyzed for several configuration of false alarm probability, SNR levels and observation time for BPSK and QPSK modulations. In the context of AMC, the reduced alpha-profile is proposed as as a cyclostationary signature calculated for a reduced cyclic frequencies set. This signature is validated by a modulation classification architecture based on pattern matching. The architecture for AMC is investigated for correct classification rates of AM, BPSK, QPSK, MSK and FSK modulations, considering several scenarios of observation length and SNR levels. The numerical results of performance obtained in this work show the eficiency of the proposed architectures

Integração de imagem aérea de alta resolução e dados de varredura a laser na classificação de cenas urbanas para detectar regiões de via

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Desenvolvimento de um sistema hierárquico de classificação com aplicação baseada em abordagem Fuzzy

Land cover mappings represent important tools for the regional planning. However, the current mappings are related to very specific purposes and, consequently, they are limited in their capacity to define the wide variety of existing types of land cover. In that context, this paper aims at developing a wide and including hierarchical classification system for land cover mapping in regional scale, which should contribute for a future standardization of classes. Besides, it is intended to test that system for a study case that contemplates the use of a classification method based on fuzzy approach, which has shown to be more appropriate than conventional approaches. Therefore, it was proposed a hierarchical classification system with three detailing levels and a study case was defined with the specification of the test area and of the classification project. Then, the georreferencing of a TM/Landsat-5 image that comprises the test area was carried out. Later, it was applied a fuzzy classification approach in the TM/Landsat-5 image, starting from images of probability for the mapped classes and an uncertainty image were generated. Finally, it was produced a conventional output that represents the thematic mapping of the test area.

Evolução e classificação do peso corporal em relação aos resultados da cirurgia bariátrica: derivação gástrica em Y de Roux

OBJETIVO: Avaliar a evolução e a classificação do peso corporal em relação aos resultados da cirurgia bariátrica em mulheres submetidas ao procedimento cirúrgico há mais de dois anos. SUJEITOS E MÉTODO: Foram avaliadas 141 mulheres submetidas à derivação gástrica em Y de Roux (DGYR) com anel de contenção. As participantes foram divididas de acordo com o tempo de pós-operatório e conforme o percentual da perda do excesso de peso (%PEP): < 50; 50 ┤ 75; e, > 75. RESULTADOS: As mulheres do grupo com %PEP < 50 (15,6%) se mantiveram obesas, enquanto aquelas que apresentaram %PEP > 75 (36,2%) situaram-se entre a eutrofia e préobesidade e tiveram menor índice de recuperação tardia de peso em relação aos demais grupos. CONCLUSÃO: A evolução de peso após dois ou mais anos da cirurgia mostrou sua esperada redução com variados graus de resposta, apontando a necessidade de monitoramento, investigação e intervenção para obtenção dos resultados esperados.

Publicação e avaliação de periódicos científicos: paradoxos da classificação Qualis em Psicologia

Recently, Brazilian scientific production has increased greatly, due to demands for productivity from scientific agencies. However, this high increases requires a more qualified production, since it s essential that publications are relevant and original. In the psychological field, the assessment scientific journals of the CAPES/ANPEPP Commission had a strong effect on the scientific community and raised questions about the chosen evaluation method. Considering this impact, the aim of this research is a meta-analysis on the assessment of Psychological journals by CAPES to update the Qualis database. For this research, Psychology scientific editors (38 questionnaires were applied by e-mail) were consulted, also 5 librarians who work with scientific journals assessment (semi-structured interviews) and 8 members who acted as referees in the CAPES/ANPEPP Commission (open questions were sent by e-mail). The results are shown through 3 analysis: general evaluation of the Qualis process (including the Assessment Committee constitution), evaluation criteria used in the process and the effect of the evaluation on the scientific community (changes on the editing scene included). Some important points emerged: disagreement among different actors about the suitability of this evaluation model; the recognition of the improvement of scientific journals, mainly toward normalization and diffusion; the verification that the model does not point the quality of the journal, i.e., the content of the scientific articles published in the journal; the disagreement with the criteria used, seemed necessary and useful but needed to be discussed and cleared between the scientific community. Despite these points, the scientific journals evaluation still is the main method to assure quality for Psychology publications

Ferramentas quimiométricas aplicadas a classificação de amostras de leite em pó e quantificação de proteínas

In this work we used chemometric tools to classify and quantify the protein content in samples of milk powder. We applied the NIR diffuse reflectance spectroscopy combined with multivariate techniques. First, we carried out an exploratory method of samples by principal component analysis (PCA), then the classification of independent modeling of class analogy (SIMCA). Thus it became possible to classify the samples that were grouped by similarities in their composition. Finally, the techniques of partial least squares regression (PLS) and principal components regression (PCR) allowed the quantification of protein content in samples of milk powder, compared with the Kjeldahl reference method. A total of 53 samples of milk powder sold in the metropolitan areas of Natal, Salvador and Rio de Janeiro were acquired for analysis, in which after pre-treatment data, there were four models, which were employed for classification and quantification of samples. The methods employed after being assessed and validated showed good performance, good accuracy and reliability of the results, showing that the NIR technique can be a non invasive technique, since it produces no waste and saves time in analyzing the samples

Organização temática da doutrina jurídica: elementos metodológicos para uma proposta de extensão da classificação decimal de direito

Specificity and updating of the bibliographic classification systems can be considered a determinant factor to the quality of organization and representation of the legal documentation. In the specific case of Brazil, the Brazilian Law Decimal Classification, does not foresee specific subdivisions for Labor Law procedures. In this sense, it carries out a terminological work based on table of contents of doctrinal Labor Law books of the mentioned area, which are compared to the conceptual structure of the Brazilian Law Decimal Classification. As a result, it presents an extension proposal for Labor Procedures as well as a methodological background for further extensions and updates.

Estudo comparativo da classificação do grau de perda auditiva em idosos institucionalizados

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Uso de confiabilidade na rotulação de exemplos em problemas de classificação multirrótulo com aprendizado semissupervisionado

The techniques of Machine Learning are applied in classification tasks to acquire knowledge through a set of data or information. Some learning methods proposed in literature are methods based on semissupervised learning; this is represented by small percentage of labeled data (supervised learning) combined with a quantity of label and non-labeled examples (unsupervised learning) during the training phase, which reduces, therefore, the need for a large quantity of labeled instances when only small dataset of labeled instances is available for training. A commom problem in semi-supervised learning is as random selection of instances, since most of paper use a random selection technique which can cause a negative impact. Much of machine learning methods treat single-label problems, in other words, problems where a given set of data are associated with a single class; however, through the requirement existent to classify data in a lot of domain, or more than one class, this classification as called multi-label classification. This work presents an experimental analysis of the results obtained using semissupervised learning in troubles of multi-label classification using reliability parameter as an aid in the classification data. Thus, the use of techniques of semissupervised learning and besides methods of multi-label classification, were essential to show the results

Prevalência, classificação e características do refluxo cecoileal diagnosticado pelo enema opaco

OBJETIVO: Verificar, retrospectivamente, a prevalência do refluxo cecoileal diagnosticado pelo enema opaco, caracterizar sua distribuição etária e sexual e classificá-lo conforme o grau de intensidade. MATERIAIS E MÉTODOS: Foram revistos 715 enemas opacos, incluindo 268 homens e 447 mulheres com idade média de 54 anos. RESULTADOS: Dos 715 casos examinados, 46,5% apresentaram refluxo cecoileal, sendo 45% do tipo leve, 37,5% do tipo moderado e 17,5% do tipo severo. Refluxo cecoileal esteve presente em 48,3% das mulheres e em 43,6% dos homens. A distribuição percentual do refluxo cecoileal por faixa etária mostrou 46,1% nos indivíduos com menos de 21 anos, 42,1% nos indivíduos entre 21-40 anos, 49,8% nos indivíduos entre 41-60 anos e 44,7% nos indivíduos com mais de 60 anos. CONCLUSÃO: Refluxo cecoileal foi achado relativamente freqüente em nosso material, correspondendo os graus moderado e severo a 25% do material examinado. Aparentemente, não há associação entre seu surgimento e sexo ou idade. A etiopatogenia e conseqüências do refluxo cecoileal são ainda pouco conhecidas. Alguns estudos sugerem que o comprometimento de componentes da junção ileocecal, como os ligamentos, pode favorecer seu aparecimento. Entre as conseqüências prováveis, incluem-se a contaminação e alteração motora ileais, resultantes do material refluído do ceco.

Detecção e diagnóstico de falhas não-supervisionados baseados em estimativa de densidade recursiva e classificador fuzzy auto-evolutivo

In this work, we propose a two-stage algorithm for real-time fault detection and identification of industrial plants. Our proposal is based on the analysis of selected features using recursive density estimation and a new evolving classifier algorithm. More specifically, the proposed approach for the detection stage is based on the concept of density in the data space, which is not the same as probability density function, but is a very useful measure for abnormality/outliers detection. This density can be expressed by a Cauchy function and can be calculated recursively, which makes it memory and computational power efficient and, therefore, suitable for on-line applications. The identification/diagnosis stage is based on a self-developing (evolving) fuzzy rule-based classifier system proposed in this work, called AutoClass. An important property of AutoClass is that it can start learning from scratch". Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for AutoClass (the number may grow, with new class labels being added by the on-line learning process), in a fully unsupervised manner. In the event that an initial rule base exists, AutoClass can evolve/develop it further based on the newly arrived faulty state data. In order to validate our proposal, we present experimental results from a level control didactic process, where control and error signals are used as features for the fault detection and identification systems, but the approach is generic and the number of features can be significant due to the computationally lean methodology, since covariance or more complex calculations, as well as storage of old data, are not required. The obtained results are significantly better than the traditional approaches used for comparison

Investigando a combinação de técnicas de aprendizado semissupervisionado e classificação hierárquica multirrótulo

Data classification is a task with high applicability in a lot of areas. Most methods for treating classification problems found in the literature dealing with single-label or traditional problems. In recent years has been identified a series of classification tasks in which the samples can be labeled at more than one class simultaneously (multi-label classification). Additionally, these classes can be hierarchically organized (hierarchical classification and hierarchical multi-label classification). On the other hand, we have also studied a new category of learning, called semi-supervised learning, combining labeled data (supervised learning) and non-labeled data (unsupervised learning) during the training phase, thus reducing the need for a large amount of labeled data when only a small set of labeled samples is available. Thus, since both the techniques of multi-label and hierarchical multi-label classification as semi-supervised learning has shown favorable results with its use, this work is proposed and used to apply semi-supervised learning in hierarchical multi-label classication tasks, so eciently take advantage of the main advantages of the two areas. An experimental analysis of the proposed methods found that the use of semi-supervised learning in hierarchical multi-label methods presented satisfactory results, since the two approaches were statistically similar results

Aplicação do Sensoriamento Remoto e do Sistema de Informações Geográficas na Detecção de Manchas de Óleo na Região do Pólo de Exploração de Guamaré, R.N.

Objective to establish a methodology for the oil spill monitoring on the sea surface, located at the Submerged Exploration Area of the Polo Region of Guamaré, in the State of Rio Grande do Norte, using orbital images of Synthetic Aperture Radar (SAR integrated with meteoceanographycs products. This methodology was applied in the following stages: (1) the creation of a base map of the Exploration Area; (2) the processing of NOAA/AVHRR and ERS-2 images for generation of meteoceanographycs products; (3) the processing of RADARSAT-1 images for monitoring of oil spills; (4) the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products; and (5) the structuring of a data base. The Integration of RADARSAT-1 image of the Potiguar Basin of day 21.05.99 with the base map of the Exploration Area of the Polo Region of Guamaré for the identification of the probable sources of the oil spots, was used successfully in the detention of the probable spot of oil detected next to the exit to the submarine emissary in the Exploration Area of the Polo Region of Guamaré. To support the integration of RADARSAT-1 images with NOAA/AVHRR and ERS-2 image products, a methodology was developed for the classification of oil spills identified by RADARSAT-1 images. For this, the following algorithms of classification not supervised were tested: K-means, Fuzzy k-means and Isodata. These algorithms are part of the PCI Geomatics software, which was used for the filtering of RADARSAT-1 images. For validation of the results, the oil spills submitted to the unsupervised classification were compared to the results of the Semivariogram Textural Classifier (STC). The mentioned classifier was developed especially for oil spill classification purposes and requires PCI software for the whole processing of RADARSAT-1 images. After all, the results of the classifications were analyzed through Visual Analysis; Calculation of Proportionality of Largeness and Analysis Statistics. Amongst the three algorithms of classifications tested, it was noted that there were no significant alterations in relation to the spills classified with the STC, in all of the analyses taken into consideration. Therefore, considering all the procedures, it has been shown that the described methodology can be successfully applied using the unsupervised classifiers tested, resulting in a decrease of time in the identification and classification processing of oil spills, if compared with the utilization of the STC classifier