Speeding up optimum-path forest training by path-cost propagation

In this paper we present an optimization of the Optimum-Path Forest classifier training procedure, which is based on a theoretical relationship between minimum spanning forest and optimum-path forest for a specific path-cost function. Experiments on public datasets have shown that the proposed approach can obtain similar accuracy to the traditional one but with faster data training. © 2012 ICPR Org Committee.

An iterated sample construction with path relinking method: Application to switch allocation in electrical distribution networks

We present a metaheuristic approach which combines constructive heuristics and local searches based on sampling with path relinking. Its effectiveness is demonstrated by an application to the problem of allocating switches in electrical distribution networks to improve their reliability. Our approach also treats the service restoration problem, which has to be solved as a subproblem, to evaluate the reliability benefit of a given switch allocation proposal. Comparisons with other metaheuristics and with a branch-and-bound procedure evaluate its performance. © 2012 Published by Elsevier Ltd.

Análisis de trayectoria (path analysis): aplicación en la demografía social con un ejemplo que emplea un programa disponible en CELADE

Incluye Bibliografía

ECG arrhythmia classification based on optimum-path forest

An important tool for the heart disease diagnosis is the analysis of electrocardiogram (ECG) signals, since the non-invasive nature and simplicity of the ECG exam. According to the application, ECG data analysis consists of steps such as preprocessing, segmentation, feature extraction and classification aiming to detect cardiac arrhythmias (i.e.; cardiac rhythm abnormalities). Aiming to made a fast and accurate cardiac arrhythmia signal classification process, we apply and analyze a recent and robust supervised graph-based pattern recognition technique, the optimum-path forest (OPF) classifier. To the best of our knowledge, it is the first time that OPF classifier is used to the ECG heartbeat signal classification task. We then compare the performance (in terms of training and testing time, accuracy, specificity, and sensitivity) of the OPF classifier to the ones of other three well-known expert system classifiers, i.e.; support vector machine (SVM), Bayesian and multilayer artificial neural network (MLP), using features extracted from six main approaches considered in literature for ECG arrhythmia analysis. In our experiments, we use the MIT-BIH Arrhythmia Database and the evaluation protocol recommended by The Association for the Advancement of Medical Instrumentation. A discussion on the obtained results shows that OPF classifier presents a robust performance, i.e.; there is no need for parameter setup, as well as a high accuracy at an extremely low computational cost. Moreover, in average, the OPF classifier yielded greater performance than the MLP and SVM classifiers in terms of classification time and accuracy, and to produce quite similar performance to the Bayesian classifier, showing to be a promising technique for ECG signal analysis. © 2012 Elsevier Ltd. All rights reserved.

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.

Ritual de consumo do crack: Aspectos socioantropológicos e repercussões para a saúde dos usuários

The use of crack cocaine has given rise to an intense discussion in society. Research has contributed to the characterization of users and the negative consequences of its use. However, few studies have conducted in-depth study of the social and cultural contexts in which crack cocaine is used. Thus, this study seeks to discuss the ritual of crack cocaine use and its social and health consequences for the user. It is a qualitative study developed in the Psychosocial Care Centers for Alcohol and Drugs (Portuguese acronym: Caps-ad). The individuals were selected in two groups of keyinformants: crack cocaine users undergoing treatment and health professionals. Data was obtained by means of semi-structured interviews. The results revealed that the use of crack cocaine is not dissociated with the current organizational structure of society. There is a link between the use of this substance and the social organization for its use. By using crack cocaine, the individuals try to be part of a consumer market, actively participating in what society perceives as new. The forms and locations of use are directly related to users' health, making it necessary for healthcare services to detect, approach and make health interventions in these locations of use.

A wrapper approach for feature selection based on Bat Algorithm and Optimum-Path Forest

Besides optimizing classifier predictive performance and addressing the curse of the dimensionality problem, feature selection techniques support a classification model as simple as possible. In this paper, we present a wrapper feature selection approach based on Bat Algorithm (BA) and Optimum-Path Forest (OPF), in which we model the problem of feature selection as an binary-based optimization technique, guided by BA using the OPF accuracy over a validating set as the fitness function to be maximized. Moreover, we present a methodology to better estimate the quality of the reduced feature set. Experiments conducted over six public datasets demonstrated that the proposed approach provides statistically significant more compact sets and, in some cases, it can indeed improve the classification effectiveness. © 2013 Elsevier Ltd. All rights reserved.

Explicacion breve del analisis de trayectoria (Path analysis) con un ejemplo que emplea un programa disponible en el terminal APL de CELADE

Incluye Bibliografía

Um algoritmo para a construção de superfícies potenciais de falha em sólidos tridimensionais

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Report of the seminar on Caribbean development thinking the path covered and the way forward

The Economic Commission for Latin America and the Caribbean (ECLAC), Subregional Headquarters for the Caribbean, and secretariat of the Caribbean Development and Cooperation Committee (CDCC) convened a Seminar on Caribbean Development Thinking: The Path Covered and the Way Forward, in Port of Spain on 21 October 2009. The meeting was attended by representatives of the following CDCC member countries: Dominica, Grenada, Guyana, Jamaica, Saint Lucia, Saint Kitts and Nevis, Saint Vincent and the Grenadines, Suriname and Trinidad and Tobago. Representatives of the following organizations of the United Nations system also attended: the International Labour Organisation (ILO); Joint United Nations Project on HIV/AIDS (UNAIDS); and the United Nations Development Programme (UNDP). The following intergovernmental organizations were represented: the Caribbean Community (CARICOM); the Caribbean Development Bank (CDB); the Caribbean Regional Negotiating Mechanism/Caribbean Community (CRNM/CARICOM); Delegation of the European Commission in Trinidad and Tobago; the Organisation of American States (OAS); and the Organisation of Eastern Caribbean States (OECS). The University of the West Indies (UWI) also participated. The list of participants appears as annex I to this report.

PATH dependency e a transformação agrária do bioma amazônico: o sentido econômico das capoeiras para o desenvolvimento sustentável

As capoeiras - áreas alteradas por ação antrópica que se encontram em estágios de regeneração espontânea de cobertura florestal - são componentes da paisagem rural de grande significado na Amazônia. No último Censo Agropecuário, as áreas de capoeira perfaziam 4,5 milhões de hectares em toda a Região Norte. Uma literatura crescentemente importante considera tais áreas proxy de economias rurais decadentes e insustentáveis, sobre as quais se ergue uma pecuária de corte eficiente e sustentável. Este artigo procura estabelecer os diferentes tipos de capoeira que se constatam na economia rural da Amazônia, associando-as às diferentes formas de produção, cujos sistemas se expressam dinamicamente como trajetórias tecnológicas concorrentes. A partir daí a) demonstra que parte dessas áreas resulta de mudanças positivas nos sistemas produtivos que produzem capoeiras com grande capacidade de regeneração – estando associada, portanto, a inovações relevantes para o desenvolvimento da Região numa perspectiva que incorpora critérios de sustentabilidade ambiental; b) demonstra que os tipos de capoeira que indicam degradação, pela baixa capacidade de regeneração, se associam à pecuária de corte, a qual na Região tem apresentado dificuldades estruturais de modernização técnica e c) indica que o ambiente institucional, favorecendo os sistemas que produzem capoeira degradada em detrimento daqueles que produzem capoeiras de rápida recomposição, podem aprisionar (levar a um lock-in) a economia agrária da região nas piores soluções, tanto econômica, quanto social e ecologicamente.

EEG signal classification for epilepsy diagnosis via optimum path forest - A systematic assessment

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A path- and label-cost propagation approach to speedup the training of the optimum-path forest classifier

In general, pattern recognition techniques require a high computational burden for learning the discriminating functions that are responsible to separate samples from distinct classes. As such, there are several studies that make effort to employ machine learning algorithms in the context of big data classification problems. The research on this area ranges from Graphics Processing Units-based implementations to mathematical optimizations, being the main drawback of the former approaches to be dependent on the graphic video card. Here, we propose an architecture-independent optimization approach for the optimum-path forest (OPF) classifier, that is designed using a theoretical formulation that relates the minimum spanning tree with the minimum spanning forest generated by the OPF over the training dataset. The experiments have shown that the approach proposed can be faster than the traditional one in five public datasets, being also as accurate as the original OPF. (C) 2014 Elsevier B. V. All rights reserved.

Fatigue crack growth rate in mode I of a carbon fiber 5HS weave composite laminate processed via RTM

Delamination or crack propagation between plies is a critical issue for structural composites. In viewing this issue and the large application of woven fabrics in structural applications, especially the ones that requires high drapeability to be preformed in a RTM mold cavity such as the asymmetric ones, e.g HS series, this research aimed in dynamically testing the carbon fiber 5HS/RTM6 epoxy composites under opening mode using DCB set up in order to investigate the crack growth rate behavior in an irregular surface produced by the fabric waviness. The evaluation of the energy involved in each crack increment was based on the Irwin-Kies equation using compliance beam theory. The tests were conducted at constant stress ratio of R=0.1 with displacement control, frequency of 10 Hz, in accordance to ASTM E647-00 for measurement of crack growth rate. The results showed large scatter when compared to unidirectional carbon fiber composites due to damage accumulation at the fill tows.