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GAODE and HAODE: Two Proposals based on AODE to Deal with Continuous Variables
"... AODE (Aggregating OneDependence Estimators) is considered one of the most interesting representatives of the Bayesian classifiers, taking into account not only the low error rate it provides but also its efficiency. Until now, all the attributes in a dataset have had to be nominal to build an AODE ..."
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AODE (Aggregating OneDependence Estimators) is considered one of the most interesting representatives of the Bayesian classifiers, taking into account not only the low error rate it provides but also its efficiency. Until now, all the attributes in a dataset have had to be nominal to build an AODE classifier or they have had to be previously discretized. In this paper, we propose two different approaches in order to deal directly with numeric attributes. One of them uses conditional Gaussian networks to model a dataset exclusively with numeric attributes; and the other one keeps the superparent on each model discrete and uses univariate Gaussians to estimate the probabilities for the numeric attributes and multinomial distributions for the categorical ones, it also being able to model hybrid datasets. Both of them obtain competitive results compared to AODE, the latter in particular being a very attractive alternative to AODE in numeric datasets. 1.
Author manuscript, published in "European Control Conference (ECC'07) (2007)" Fault diagnosis of industrial systems with bayesian networks and mutual information
, 2010
"... Abstract — The purpose of this article is to present two new methods for industrial process diagnosis. These two methods are based on the use of a bayesian network. An identification of important variables is made by computing the mutual information between each variable of the system and the class ..."
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Abstract — The purpose of this article is to present two new methods for industrial process diagnosis. These two methods are based on the use of a bayesian network. An identification of important variables is made by computing the mutual information between each variable of the system and the class variable. The performances of the two methods are evaluated on the data of a benchmark example: the Tennessee Eastman Process. Three kinds of fault are taken into account on this complex process. The challenging objective is to obtain the minimal recognition error rate for these three faults. Results are given and compared on the same data with those of other published methods. I.
Author manuscript, published in "Bayesian Networks Sciyo (Ed.) (2010)" Monitoring of complex processes with Bayesian networks
, 2010
"... Industrial processes are more and more complex and include a lot of sensors giving measurements of some attributes of the system. A study of these measurements can allow to decide on the correct working conditions of the process. If the process is not in normal working conditions, it signifies that ..."
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Industrial processes are more and more complex and include a lot of sensors giving measurements of some attributes of the system. A study of these measurements can allow to decide on the correct working conditions of the process. If the process is not in normal working conditions, it signifies that a fault has occurred in the process. If no fault has occurred, thus the
Author manuscript, published in "American Control Conference (ACC'07) (2007)" Procedure based on mutual information and bayesian networks for the fault diagnosis of industrial systems
, 2010
"... Abstract — The aim of this paper is to present a new method for process diagnosis using a bayesian network. The mutual information between each variable of the system and the class variable is computed to identify the important variables. To illustrate the performances of this method, we use the Ten ..."
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Abstract — The aim of this paper is to present a new method for process diagnosis using a bayesian network. The mutual information between each variable of the system and the class variable is computed to identify the important variables. To illustrate the performances of this method, we use the Tennessee Eastman Process. For this complex process (51 variables), we take into account three kinds of faults with the minimal recognition error rate objective. I.
International Journal of Uncertainty, Fuzziness and KnowledgeBased Systems c © World Scientific Publishing Company SELECTIVE NAIVE BAYES FOR REGRESSION BASED ON MIXTURES OF TRUNCATED EXPONENTIALS∗
, 2007
"... Naive Bayes models have been successfully used in classification problems where the class variable is discrete. These models have also been applied to regression or prediction problems, i.e. classification problems where the class variable is continuous, but usually under the assumption that the joi ..."
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Naive Bayes models have been successfully used in classification problems where the class variable is discrete. These models have also been applied to regression or prediction problems, i.e. classification problems where the class variable is continuous, but usually under the assumption that the joint distribution of the feature variables and the class is multivariate Gaussian. In this paper we are interested in regression problems where some of the feature variables are discrete while the others are continuous. We propose a Naive Bayes predictor based on the approximation of the joint distribution by a Mixture of Truncated Exponentials (MTE). We have followed a filterwrapper procedure for selecting the variables to be used in the construction of the model. This scheme is based on the mutual information between each of the candidate variables and the class. Since the mutual information can not be computed exactly for the MTE distribution, we introduce an unbiased estimator of it, based on Monte Carlo methods. We test the performance of the proposed model in artificial and realworld datasets.
Dynamic Analysis of Upper Limbs Movement of Breast Cancer Patients
, 2015
"... O cancro da mama é o principal tipo de cancro em mulheres, possuindo uma elevada taxa de sobrevivência. No entanto, os tratamentos habituais (radioterapia ou remoção cirúrgica dos nódulos linfáticos da axila, por exemplo) tendem a levar a uma diminuição da qualidade de vida da paciente, já que pode ..."
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O cancro da mama é o principal tipo de cancro em mulheres, possuindo uma elevada taxa de sobrevivência. No entanto, os tratamentos habituais (radioterapia ou remoção cirúrgica dos nódulos linfáticos da axila, por exemplo) tendem a levar a uma diminuição da qualidade de vida da paciente, já que pode conduzir à diminuição da funcionalidade dos membros superiores. Para diminuir o impacto destes problemas na qualidade de vida da paciente, tal como para prevenir futuras complicações, é importante uma detecção prematura de lesões. Esta detecção é tradicionalmente realizada por métodos subjectivos, desde a medição de volume, medição de ângulos ou inquéritos. Com este trabalho pretendese a criação de um método objectivo contrariando a prática habitual que permita realizar uma análise mais correcta da condição física da paciente e os problemas associados ao membro superior. Para tal, procedeuse à selecção de um conjunto de exercícios a realizar pela paciente, tendose adquirido dados RGBD e Skeleton Tracking durante os mesmos. Os dados adquiridos foram processados de forma a obterse melhores características dos mesmos. Assim, para os dados provenientes do Skeleton, foram avaliados os impactos de cinco diferentes filtros e comparados com um ground truth marcado manualmente. Após esta avaliação, o
Bayesian Conditional Gaussian Network Classifiers with Applications to Mass Spectra Classification
, 2014
"... Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides ..."
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Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal predictions and is known to be robust to overfitting. In this work we introduce Bayesian Conditional Gaussian Network Classifiers, which efficiently perform exact Bayesian averaging over the parameters. We evaluate the proposed classifiers against the maximum likelihood alternatives proposed so far over standard UCI datasets, concluding that performing BA improves the quality of the assessed probabilities (conditional log likelihood) whilst maintaining the error rate. Overfitting is more likely to occur in domains where the number of data items is small and the number of variables is large. These two conditions are met in the realm of bioinformatics, where the early diagnosis of cancer from mass spectra is a relevant task. We provide an 1 ar
Fuzzy Naive Bayesian for constructing regulated network with weights
"... Abstract. In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this ..."
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Abstract. In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted membership function (NEWFM) to extract regulated relations and weights. Then, we can use regulated relations and weights to construct a regulated network. Finally, we will classify the heart and Haberman datasets by the FNB network to compare with experiments of Naive Bayesian and TAN. The experiment results show that the FNB has a higher classification rate than Naive Bayesian and TAN.
Selective Naive Bayes Predictor with Mixtures of Truncated Exponentials
"... Naive Bayes models have been successfully used in classification problems where the class variable is discrete. Naive Bayes models have been applied to regression or prediction problems, i.e. classification problems with continuous class, but usually under the assumption that the joint distribution ..."
Abstract
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Naive Bayes models have been successfully used in classification problems where the class variable is discrete. Naive Bayes models have been applied to regression or prediction problems, i.e. classification problems with continuous class, but usually under the assumption that the joint distribution of the feature variables and the class is multivariate Gaussian. In this paper we are interested in regression problems where some of the feature variables are discrete while the others are continuous. We propose a Naive Bayes predictor based on the approximation of the joint distribution by a Mixture of Truncated Exponentials (MTE). We have designed a procedure for selecting the variables that should be used in the construction of the model. This scheme is based on the mutual information between each of the candidate variables and the class. Since the mutual information can not be computed exactly for the MTE distribution, we introduce an unbiased estimator of it, based on Monte Carlo methods. We test the performance of the proposed model in three real life problems, related to higher education management.