Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. In a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. In addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.

Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks

Semi-qualitative probabilistic networks (SQPNs) merge two important graphical model formalisms: Bayesian networks and qualitative probabilistic networks. They provade a very Complexity of inferences in polytree-shaped semi-qualitative probabilistic networks and qualitative probabilistic networks. They provide a very general modeling framework by allowing the combination of numeric and qualitative assessments over a discrete domain, and can be compactly encoded by exploiting the same factorization of joint probability distributions that are behind the bayesian networks. This paper explores the computational complexity of semi-qualitative probabilistic networks, and takes the polytree-shaped networks as its main target. We show that the inference problem is coNP-Complete for binary polytrees with multiple observed nodes. We also show that interferences can be performed in time linear in the number of nodes if there is a single observed node. Because our proof is construtive, we obtain an efficient linear time algorithm for SQPNs under such assumptions. To the best of our knowledge, this is the first exact polynominal-time algorithm for SQPn. Together these results provide a clear picture of the inferential complexity in polytree-shaped SQPNs.

Bagging k-dependence probabilistic networks: An alternative powerful fraud detection tool

Fraud is a global problem that has required more attention due to an accentuated expansion of modern technology and communication. When statistical techniques are used to detect fraud, whether a fraud detection model is accurate enough in order to provide correct classification of the case as a fraudulent or legitimate is a critical factor. In this context, the concept of bootstrap aggregating (bagging) arises. The basic idea is to generate multiple classifiers by obtaining the predicted values from the adjusted models to several replicated datasets and then combining them into a single predictive classification in order to improve the classification accuracy. In this paper, for the first time, we aim to present a pioneer study of the performance of the discrete and continuous k-dependence probabilistic networks within the context of bagging predictors classification. Via a large simulation study and various real datasets, we discovered that the probabilistic networks are a strong modeling option with high predictive capacity and with a high increment using the bagging procedure when compared to traditional techniques. (C) 2012 Elsevier Ltd. All rights reserved.

Scientific research output evaluation of professors of Sao Paulo State University, Marilia/SP

In this work, an analysis of scientific bibliographic productivity was made using the Faculdade de Filosofia e Ciencias, Universidade Estadual Paulista (FFC-UNESP) as example. It is composed by nine departments which offer altogether nine undergraduate courses: 1) Archival, 2) Library, 3) Speech Therapy, 4) Pedagogy, 5) International Relations, 6) Physiotherapy, 7) Occupational Therapy, 8) Philosophy, 9) Social Sciences and six graduate programs leading to M. S. and Ph.D. degrees. Moreover, when analyzing the different courses of FFC-UNESP, they represent typical academic organization in Brazil and Latin America and could be taken as a model for analyzing other Brazilian research institutions. Using data retrieved from the Lattes Plataform database (Curriculum Lattes) we have quantitatively the scientific productivity percentage of professors at UNESP. We observed that bibliometric evaluations using the Curriculum Lattes (CL) showed that the professors published papers in journal are not indexed by ISI and SCOPUS. This analysis was made using: 1) the total number of papers (indexed in Curriculum Lattes database), 2) the number of papers indexed by Thomson ISI Web of Science database and SCOPUS database, and 3) the Hirsch (h-index) by ISI and SCOPUS. Bibliometric evaluations of departments showed a better performance of Political Science and Economics Department when compared to others departments, in relation total number of papers (indexed in Curriculum Lattes database). We also analyzed the academic advisory (Master's Thesis and Ph. D. Thesis) by nine departments of FFC/UNESP. The Administration and School Supervision Department presented a higher academic advisory (concluded and current) when compared to the others departments.

Alocação eficiente de indicadores de faltas em um sistema de distribuição real usando computação evolutiva

Este trabalho propõe uma abordagem computacional evolutiva para a resolução do problema de alocação de dispositivos indicadores de faltas (IFs) em alimentadores primários de distribuição de energia elétrica. De forma mais específica, o problema de se obter o melhor local de instalação é solucionado por meio da técnica de Algoritmos Genéticos (AGs) que busca obter uma configuração eficiente de instalação de IFs no tronco principal de um alimentador de distribuição. Assim, faz-se a modelagem do mesmo na forma de um problema de otimização orientado à melhoria dos indicadores de qualidade do serviço e ao encontro de uma solução economicamente atraente. Os resultados com dados reais comprovam a eficiência da metodologia proposta.