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.

Experience generalization for multi-agent reinforcement learning

On-line learning methods have been applied successfully in multi-agent systems to achieve coordination among agents. Learning in multi-agent systems implies in a non-stationary scenario perceived by the agents, since the behavior of other agents may change as they simultaneously learn how to improve their actions. Non-stationary scenarios can be modeled as Markov Games, which can be solved using the Minimax-Q algorithm a combination of Q-learning (a Reinforcement Learning (RL) algorithm which directly learns an optimal control policy) and the Minimax algorithm. However, finding optimal control policies using any RL algorithm (Q-learning and Minimax-Q included) can be very time consuming. Trying to improve the learning time of Q-learning, we considered the QS-algorithm. in which a single experience can update more than a single action value by using a spreading function. In this paper, we contribute a Minimax-QS algorithm which combines the Minimax-Q algorithm and the QS-algorithm. We conduct a series of empirical evaluation of the algorithm in a simplified simulator of the soccer domain. We show that even using a very simple domain-dependent spreading function, the performance of the learning algorithm can be improved.

Particle Competition and Cooperation in Networks for Semi-Supervised Learning with Concept Drift

Concept drift is a problem of increasing importance in machine learning and data mining. Data sets under analysis are no longer only static databases, but also data streams in which concepts and data distributions may not be stable over time. However, most learning algorithms produced so far are based on the assumption that data comes from a fixed distribution, so they are not suitable to handle concept drifts. Moreover, some concept drifts applications requires fast response, which means an algorithm must always be (re) trained with the latest available data. But the process of labeling data is usually expensive and/or time consuming when compared to unlabeled data acquisition, thus 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 also based on the assumption that the data is static. Therefore, semi-supervised learning with concept drifts is still an open challenge in machine learning. Recently, a particle competition and cooperation approach was used to realize graph-based semi-supervised learning from static data. In this paper, we extend that approach to handle data streams and concept drift. The result is a passive algorithm using a single classifier, which naturally adapts to concept changes, without any explicit drift detection mechanism. Its built-in mechanisms provide a natural way of learning from new data, gradually forgetting older knowledge as older labeled data items became less influent on the classification of newer data items. Some computer simulation are presented, showing the effectiveness of the proposed method.

The assessment of the quality of sugar using electronic tongue and machine learning algorithms

The correct classification of sugar according to its physico-chemical characteristics directly influences the value of the product and its acceptance by the market. This study shows that using an electronic tongue system along with established techniques of supervised learning leads to the correct classification of sugar samples according to their qualities. In this paper, we offer two new real, public and non-encoded sugar datasets whose attributes were automatically collected using an electronic tongue, with and without pH controlling. Moreover, we compare the performance achieved by several established machine learning methods. Our experiments were diligently designed to ensure statistically sound results and they indicate that k-nearest neighbors method outperforms other evaluated classifiers and, hence, it can be used as a good baseline for further comparison. © 2012 IEEE.

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.

Effects of Cigarette Smoking Intensity on the Mucociliary Clearance of Active Smokers

Background: Smoking impairs mucociliary clearance and increases respiratory infection frequency and severity in subjects with and without smoking-related chronic lung diseases. Objective: This study evaluated the effects of smoking intensity on mucociliary clearance in active smokers. Methods: Seventy-five active smokers were grouped into light (1-10 cigarettes/day; n = 14), moderate (11-20 cigarettes/day; n = 34) and heavy smokers (≥21 cigarettes/day; n = 27) before starting a smoking cessation programme. Smoking behaviour, nicotine dependence, pulmonary function, carbon monoxide in exhaled air (exCO), carboxyhaemoglobin (COHb) and mucociliary clearance measured by the saccharin transit time (STT) test were all evaluated. An age-matched non-smoker group (n = 24) was assessed using the same tests. Results: Moderate (49 ± 7 years) and heavy smokers (46 ± 8 years) had higher STT (p = 0.0001), exCO (p < 0.0001) and COHb (p < 0.0001) levels compared with light smokers (51 ± 15 years) and non-smokers (50 ± 11 years). A positive correlation was observed between STT and exCO (r = 0.4; p < 0.0001), STT and cigarettes/day (r = 0.3, p = 0.02) and exCO and cigarettes/day (r = 0.3, p < 0.01). Conclusion: Smoking impairs mucociliary clearance and is associated with cigarette smoking intensity. Copyright © 2013 S. Karger AG, Basel.

Desenvolvimento de sistema de educação continuada a distância e interconsulta em hepatites virais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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)

Efeito do desenho em amostragem de conglomerado para estimar a distribuição de ocupações entre trabalhadores

OBJETIVO: Descrever o desenho amostral efetuado para estimar a distribuição de população economicamente ativa, apresentando o efeito do desenho encontrado. MÉTODOS: A partir de um cadastro universal, foram amostrados 4.782 domicílios residenciais do município de Botucatu, SP, por intermédio de amostra aleatória sistemática de conglomerados, realizada entre junho e julho de 1997. RESULTADOS: Os 4.782 domicílios residenciais corresponderam a 17.219 moradores de Botucatu, entre junho e julho de 1997. em decorrência da perda de heterogeneidade da distribuição das ocupações dentro dos domicílios amostrados, o efeito do desenho encontrado variou entre 1,00 e 1,96. CONCLUSÕES: Com base nos resultados obtidos, sugere-se que, em amostras de conglomerados para estimativas da distribuição de ocupações em populações economicamente ativas, o efeito do desenho seja estimado como e=1,50, para amostragens em zona urbana; e e=2,00 para amostragens em zona rural.

Depressive symptoms and level of physical activity in patients with Alzheimer's disease

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

Concepções de qualidade de ensino dos coordenadores de graduação: uma análise dos cursos de odontologia do Estado de São Paulo

As novas demandas sociais e as diretrizes curriculares brasileiras para os cursos de odontologia colocam desafios à prática docente nas instituições de educação superior. Nesse contexto, investigam-se as concepções de qualidade de ensino universitário de professores que atuam como coordenadores de graduação nas faculdades de odontologia do Estado de São Paulo que possuem pós-graduação stricto-sensu, para refletir sobre os desafios da formação docente na área. Como instrumentos de levantamento de dados utilizou-se questionário, contendo perguntas abertas e fechadas e entrevista semi-estruturada, organizada para possibilitar o aprofundamento da discussão. Os dados foram descritos e discutidos mediante análise quantitativa e qualitativa, a partir das três dimensões da prática docente analisadas por Cunha (1995): político-estrutural, curricular e pedagógica. Para este artigo, focalizaram-se apenas os aspectos da dimensão pedagógica, na qual os pontos que expressam posturas mais contraditórias referem-se a métodos de ensino-aprendizagem, participação do aluno e tutoria. Os resultados apontam para concepções de ensino-aprendizagem que oscilam entre modelos tradicionais e inovadores, sinalizando pontos de conflito em relação a paradigmas que se articulam diretamente a questões curriculares e político-estruturais.

Evaluación de las metodologías de enseñanza-aprendizaje de la bioética en las facultades de odontología Brasileñas

Introduction. Bioethics is present in Dentistry and other health areas, as scientific research that results on profession progress, as in proper clinical attendance. The study consists on valuation of teaching-learning methods of Bioethical on Brazilian Dental Schools. Materials and methods. Data collect occurred by semi-structured questioners send by e-mail and correspondence. It was realized descriptive analysis of quantitative answers, and for qualitative answers, it was used content analysis, by categorical analysis technique in according to Bardin. Results. Among 182 Dental Schools actives in Brazil, only 57 (31.3%) showed bioethical discipline in its curricular grid. It was observed the discipline is teaching on theoretical form (77.8%). Principal forms of evaluation are: writing prove (100%) and seminaries (75%). Just 6.4% of professors use bibliographic references about bioethical faced to Odontology specifically. The majority of interviewed person (74.2%) considered that bioethics is related on the direct and indirect form with all others disciplines. In relation to importance of Bioethical on dental surgeon formation, 64.7% emphasized it in professional-patient relation. Conclusion. The Bioethics show teaching and practice method of conserver evaluation, and so, it's necessary others methods directed to reflection of actual problems in odontology area that contribute significantly on integral formation of dental surgeon. © Viguera Editores SL 2009.

OPF-MRF: Optimum-path forest and Markov random fields for contextual-based image classification

Some machine learning methods do not exploit contextual information in the process of discovering, describing and recognizing patterns. However, spatial/temporal neighboring samples are likely to have same behavior. Here, we propose an approach which unifies a supervised learning algorithm - namely Optimum-Path Forest - together with a Markov Random Field in order to build a prior model holding a spatial smoothness assumption, which takes into account the contextual information for classification purposes. We show its robustness for brain tissue classification over some images of the well-known dataset IBSR. © 2013 Springer-Verlag.

Tabagismo e mecanismos de defesa: resposta imune e transporte mucociliar

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)