Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Particle competition and cooperation to prevent error propagation from mislabeled data in semi-supervised learning

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. This paper aims to address this problem by presenting a graph-based (network-based) semi-supervised learning method, specifically designed to handle data sets with mislabeled samples. The method uses teams of walking particles, with competitive and cooperative behavior, for label propagation in the network constructed from the input data set. The proposed model is nature-inspired and it incorporates some features to make it robust to a considerable amount of mislabeled data items. Computer simulations show the performance of the method in the presence of different percentage of mislabeled data, in networks of different sizes and average node degree. Importantly, these simulations reveals the existence of the critical points of the mislabeled subset size, below which the network is free of wrong label contamination, but above which the mislabeled samples start to propagate their labels to the rest of the network. Moreover, numerical comparisons have been made among the proposed method and other representative graph-based semi-supervised learning methods using both artificial and real-world data sets. Interestingly, the proposed method has increasing better performance than the others as the percentage of mislabeled samples is getting larger. © 2012 IEEE.

O estágio supervisionado na licenciatura em matemática: possibilidades de colaboração

A presente dissertação é o resultado de uma investigação qualitativa que tem como objeto de estudo o Estágio Supervisionado na Licenciatura em Matemática. Foi desenvolvida junto ao Programa de Pós-graduação em Educação em Ciências e Matemáticas (PPGECM) do Núcleo de Pesquisa e Desenvolvimento da Educação Matemática e Científica (NPDEMC), da Universidade Federal do Pará (UFPA). A pesquisa busca discutir as possibilidades de desenvolvimento de práticas colaborativas nos estágio supervisionado em matemática, considerando as interações existentes entre a tríade licenciando/professor-formador/professor-escolar, concebendo o estágio como um elo de ligação entre a escola e a universidade. A investigação deu-se no período de maio a outubro de 2006, envolvendo três licenciandos de uma turma de Prática de Ensino/Estágio Supervisionado da Licenciatura Plena em Matemática da UFPA. Para analisar as possibilidades e limitações de constituição de práticas ou grupos colaborativos, dentro do Estágio Supervisionado em Matemática, buscando descrever a aproximação e contribuição entre o professor-formador, o licenciando e o professor-escolar, recorreu-se aos relatórios dos licenciandos e entrevistas semi-estruturadas realizadas com os professores-escolares e licenciandos. O referencial teórico está baseado em estudos e pesquisas de autores como Gonçalves (2000; 2006), Fiorentini (2004), Ferreira (2003), Garcia (1995), Gauthier et al (1998), Tardif (2002) e Pimenta e Lima (2004) dentro do contexto da formação de professores. Neste contexto pode-se contemplar possibilidades de produção e sistematização de conhecimentos, dando ênfase a um movimento que busque dar sentido àquilo que é produzido, tanto na escola, quanto na universidade, requerendo a criação e recriação dos saberes experienciais, mas almejando adentrar num coletivo de profissionais que discutam os problemas conjuntamente, inaugurando assim, a institucionalização de práticas, atitudes, crenças, que possibilitem a todos a compreensão do que sejam os saberes da ação pedagógica.

Representações sociais da Psicologia do Trabalho: o olhar de formandos em Psicologia da Universidade Federal do Pará

Este estudo percorreu questões sociais contemporâneas que envolveram o Sujeito, o Trabalho e a Psicologia na perspectiva das Representações Sociais: A formação do psicólogo do trabalho na Universidade Federal do Pará. A pesquisa identificou a Psicologia do Trabalho na visão dos estudantes concluintes do curso de Psicologia da UFPA. Analisou-se as questões paradigmáticas que a área está inserida, as vivências dos formandos nos estágios supervisionados, a questão do trabalho como fonte de identidade e as mudanças da pós-modernidade para os futuros profissionais da Psicologia do Trabalho, assim como suas perspectivas no mercado profissional. Embasou-se em Serge Moscovici (1978, 2001, 2005, 2007) e na grande teoria das Representações Sociais-RS, e em Denise Jodelet (2001, 2005, 2009) com a abordagem processual em Representações Sociais. As RS se inseriram como a teoria fundante da temática, que privilegiou as relações cotidianas a partir das comunicações humanas, que foram formadoras de representações, para a proposição de uma nova perspectiva da Psicologia Social. Optou-se pela pesquisa qualitativa e utilizou-se o Método da Explicitação do Discurso Subjacente – MEDS, para os instrumentos de coleta e análise de dados. As análises das representações apontaram para as diversas dificuldades encontradas pelos estudantes que buscavam os estágios: a substituição de atividades de estágio por pesquisas específicas sobre o mundo do trabalho e exercícios acadêmicos on line, por meio da internet; entendiam que a apropriação desses conhecimentos substitutos seriam inadequados e impróprios a alunos em formação; informaram desejar diferenciar-se dos padrões estabelecidos na área da Psicologia do Trabalho, ou seja, da visão deturpada de psicólogos que estavam a serviço da empresa e se esqueceram do trabalhador. Na questão da prática profissional, mostraram que a tentativa dos professores, em substituir os estágios por outras atividades, implicava uma ruptura do compromisso de ensino com o viés interdisciplinar e de caráter prático, explicitados nas diretrizes curriculares dos cursos de graduação da instituição. Sentiam-se desamparados e despreparados para atuação no mercado e bastante preocupados com as exigências dessa área. Todavia, vislumbravam possibilidades quando sugeriam que este campo apresentaria potencialidades inovadoras e de constante atualização, em que o papel da universidade seria fundamental e a preparação dos professores essencial à construção de uma Psicologia do Trabalho com compromisso ético e, que emergisse da prática concreta dos futuros psicólogos formados pela instituição.

Combined active and semi-supervised learning using particle walking temporal dynamicscombined active and semi-supervised learning using particle walking temporal dynamics

Both Semi-Supervised Leaning and Active Learning are techniques used when unlabeled data is abundant, but the process of labeling them is expensive and/or time consuming. In this paper, those two machine learning techniques are combined into a single nature-inspired method. It features particles walking on a network built from the data set, using a unique random-greedy rule to select neighbors to visit. The particles, which have both competitive and cooperative behavior, are created on the network as the result of label queries. They may be created as the algorithm executes and only nodes affected by the new particles have to be updated. Therefore, it saves execution time compared to traditional active learning frameworks, in which the learning algorithm has to be executed several times. The data items to be queried are select based on information extracted from the nodes and particles temporal dynamics. Two different rules for queries are explored in this paper, one of them is based on querying by uncertainty approaches and the other is based on data and labeled nodes distribution. Each of them may perform better than the other according to some data sets peculiarities. Experimental results on some real-world data sets are provided, and the proposed method outperforms the semi-supervised learning method, from which it is derived, in all of them.

Semi-supervised learning with concept drift using particle dynamics applied to network intrusion detection data

Concept drift, which refers to non stationary learning problems over time, has increasing importance in machine learning and data mining. Many concept drift applications require fast response, which means an algorithm must always be (re)trained with the latest available data. But the process of data labeling is usually expensive and/or time consuming when compared to acquisition of unlabeled data, thus usually only a small fraction of the incoming data may be effectively labeled. Semi-supervised learning methods may help in this scenario, as they use both labeled and unlabeled data in the training process. However, most of them are based on assumptions that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenging task in machine learning. Recently, a particle competition and cooperation approach has been developed to realize graph-based semi-supervised learning from static data. We have extend that approach to handle data streams and concept drift. The result is a passive algorithm which uses a single classifier approach, naturally adapted to concept changes without any explicit drift detection mechanism. It has built-in mechanisms that provide a natural way of learning from new data, gradually "forgetting" older knowledge as older data items are no longer useful for the classification of newer data items. The proposed algorithm is applied to the KDD Cup 1999 Data of network intrusion, showing its effectiveness.

Particle competition and cooperation for semi-supervised learning with label noise

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

Supervised variational relevance learning, an analytic geometric feature selection with applications to omic datasets

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

Semi-supervised learning for relevance feedback on image retrieval tasks

Relevance feedback approaches have been established as an important tool for interactive search, enabling users to express their needs. However, in view of the growth of multimedia collections available, the user efforts required by these methods tend to increase as well, demanding approaches for reducing the need of user interactions. In this context, this paper proposes a semi-supervised learning algorithm for relevance feedback to be used in image retrieval tasks. The proposed semi-supervised algorithm aims at using both supervised and unsupervised approaches simultaneously. While a supervised step is performed using the information collected from the user feedback, an unsupervised step exploits the intrinsic dataset structure, which is represented in terms of ranked lists of images. Several experiments were conducted for different image retrieval tasks involving shape, color, and texture descriptors and different datasets. The proposed approach was also evaluated on multimodal retrieval tasks, considering visual and textual descriptors. Experimental results demonstrate the effectiveness of the proposed approach.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning

Semi-supervised learning is one of the important topics in machine learning, concerning with pattern classification where only a small subset of data is labeled. In this paper, a new network-based (or graph-based) semi-supervised classification model is proposed. It employs a combined random-greedy walk of particles, with competition and cooperation mechanisms, to propagate class labels to the whole network. Due to the competition mechanism, the proposed model has a local label spreading fashion, i.e., each particle only visits a portion of nodes potentially belonging to it, while it is not allowed to visit those nodes definitely occupied by particles of other classes. In this way, a "divide-and-conquer" effect is naturally embedded in the model. As a result, the proposed model can achieve a good classification rate while exhibiting low computational complexity order in comparison to other network-based semi-supervised algorithms. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Semi-supervised learning guided by the modularity measure in complex networks

Semi-supervised learning techniques have gained increasing attention in the machine learning community, as a result of two main factors: (1) the available data is exponentially increasing; (2) the task of data labeling is cumbersome and expensive, involving human experts in the process. In this paper, we propose a network-based semi-supervised learning method inspired by the modularity greedy algorithm, which was originally applied for unsupervised learning. Changes have been made in the process of modularity maximization in a way to adapt the model to propagate labels throughout the network. Furthermore, a network reduction technique is introduced, as well as an extensive analysis of its impact on the network. Computer simulations are performed for artificial and real-world databases, providing a numerical quantitative basis for the performance of the proposed method.

Cost-effectiveness of conventional and modified supervised toothbrushing in preventing caries in permanent molars among 5-year-old children

The cost-effectiveness of a modified supervised toothbrushing program was compared to a conventional program. A total of 284 five-year-old children presenting at least one permanent molar with emerged/sound occlusal surface participated. In the control group, oral health education and dental plaque dying followed by toothbrushing with fluoride dentifrice was carried outfour times per year. With the test group, children also underwent professional cross-brushing on surfaces of first permanent molar rendered by a dental assistant five times per year. Enamel/dentin caries were recorded on buccal, occlusal and lingual surfaces of permanent molars for a period of 18 months. The incidence density (ID) ratio was estimated using Poisson's regression model. The ID was 50% lower among boys in the test group (p = 0.016). The cost of the modified program was US$ 1.79 per capita. The marginal cost-effectiveness ratio among boys was US$ 6.30 per avoided carie. The modified supervised toothbrushing program was shown to be cost-effective in the case of boys.

Demographic characterization of supervised dog and cat populations in Sao Paulo city, Brazil

This study aimed to estimate the owned dog and cat populations of Sao Paulo city using a complex sample with random selection in two stages. In each administrative district, six census sectors and 20 households in each sampled sector were visited from September 2006 to September 2009, totalizing 11,272 interviews. The human: dog ratio was 4.34 and the human: cat ratio was 19.33. The dog population was 2,507,401 and the cat population, 562,965. The dog population was composted of 52.7% males, while among the cat population it was 45.1%. The proportion of sterilized females (23.4% among dogs and 46.1% among cats) was higher than males (11.4% among dogs and 31.5% among cats). The mean age of dogs was 4.99 years, and for cats, 3.53 years. The proportion of restricted (without access to the street) dogs, 64.4%, was higher than restricted cats, 42.5%. The average number of animals/household was 1.60 for dogs and 1.69 for cats. The animal ownership is associated with cultural factors, therefore the characterization of canine and feline population is essential to implement an adequate animal population management program and zoonosis control.

Semi-supervised dimensionality reduction based on partial least squares for visual analysis of high dimensional data

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.

Efficacy of graded activity versus supervised exercises in patients with chronic non-specific low back pain: protocol of a randomised controlled trial

Abstract Background Low back pain is a relevant public health problem, being an important cause of work absenteeism worldwide, as well as affecting the quality of life of sufferers and their individual functional performances. Supervised active physical routines and of cognitive-behavioral therapies are recommended for the treatment of chronic Low back pain, although evidence to support the effectiveness of different techniques is missing. Accordingly, the aim of this study is to contrast the effectiveness of two types of exercises, graded activity or supervised, in decreasing symptoms of chronic low back pain. Methods/design Sample will consist of 66 patients, blindly allocated into one of two groups: 1) Graded activity which, based on an operant approach, will use time-contingent methods aiming to increase participants’ activity levels; 2) Supervised exercise, where participants will be trained for strengthening, stretching, and motor control targeting different muscle groups. Interventions will last one hour, and will happen twice a week for 6 weeks. Outcomes (pain, disability, quality of life, global perceived effect, return to work, physical activity, physical capacity, and kinesiophobia) will be assessed at baseline, at treatment end, and three and six months after treatment end. Data collection will be conducted by an investigator blinded to treatment allocation. Discussion This project describes the randomisation method that will be used to compare the effectiveness of two different treatments for chronic low back pain: graded activity and supervised exercises. Since optimal approach for patients with chronic back pain have yet not been defined based on evidence, good quality studies on the subject are necessary. Trial registration NCT01719276