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Building probabilistic networks: where do the numbers come from?  a guide to the literature
 IEEE Transactions on Knowledge and Data Engineering
, 2000
"... Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision, ..."
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Cited by 32 (3 self)
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Probabilistic networks are now fairly well established as practical representations of knowledge for reasoning under uncertainty, as demonstrated by an increasing number of successful applications in such domains as (medical) diagnosis and prognosis, planning, vision,
The Use of Bayesian Network for Web Effort Estimation
 Proceedings of ICWE'07
, 2007
"... The objective of this paper is to further investigate the use of Bayesian Networks (BN) for Web effort estimation when using a crosscompany dataset. Four BNs were built; two automatically using the Hugin tool with two training sets; two using a structure elicited by a domain expert, with parameters ..."
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The objective of this paper is to further investigate the use of Bayesian Networks (BN) for Web effort estimation when using a crosscompany dataset. Four BNs were built; two automatically using the Hugin tool with two training sets; two using a structure elicited by a domain expert, with parameters obtained from automatically fitting the network to the same training sets used in the automated elicitation (hybrid models). The accuracy of all four models was measured using two validation sets, and point estimates. As a benchmark, the BNbased predictions were also compared to predictions obtained using Manual StepWise Regression (MSWR), and CaseBased Reasoning (CBR). The BN model generated using Hugin presented similar accuracy to CBR and Mean effortbased predictions. Our results suggest that Hybrid BN models can provide significantly superior prediction accuracy. However, good results also seem to depend on characteristics of the training and validation sets used. 1.
Predicting Web Development Effort Using a Bayesian Network
"... OBJECTIVE – The objective of this paper is to investigate the use of a Bayesian Network (BN) for Web effort estimation. METHOD – We built a BN automatically using the HUGIN tool and data on 120 Web projects from the Tukutuku database. In addition the BN model and node probability tables were also va ..."
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OBJECTIVE – The objective of this paper is to investigate the use of a Bayesian Network (BN) for Web effort estimation. METHOD – We built a BN automatically using the HUGIN tool and data on 120 Web projects from the Tukutuku database. In addition the BN model and node probability tables were also validated by a Web project manager from a wellestablished Web company in Rio de Janeiro (Brazil). The accuracy was measured using data on 30 projects (validation set), and point estimates (1fold crossvalidation using a 80%20 % split). The estimates obtained using the BN were also compared to estimates obtained using forward stepwise regression (SWR) as this is one of the most frequently used techniques for software and Web effort estimation. RESULTS – Our results showed that BNbased predictions were better than previous predictions from Webbased crosscompany models, and significantly better than predictions using SWR. CONCLUSIONS – Our results suggest that, at least for the dataset used, the use of a model that allows the representation of uncertainty, inherent in effort estimation, can outperform other commonly used models, such as those built using multivariate regression techniques. Web effort estimation, Bayesian networks, Forward stepwise regression, prediction accuracy. 1.
Fusion of Expert Knowledge with Data using Belief Functions: A Case Study . . .
 PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON INFORMATION FUSION
, 2002
"... This paper presents a methodology for combining expert knowledge with information from statistical data, in classification and prediction problems. The method is based on (1) a casebased approach allowing to predict a quantity of interest from past cases in the form of a belief function, (2) Bayesi ..."
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This paper presents a methodology for combining expert knowledge with information from statistical data, in classification and prediction problems. The method is based on (1) a casebased approach allowing to predict a quantity of interest from past cases in the form of a belief function, (2) Bayesian networks for modelling expert knowledge and (3) a tuning mechanism allowing to optimally discount information sources by optimizing a performance criterion. This methodology is applied to the prediction of chemical oxygen demand solubility in wastewater. The approach is expected to be useful in situations where both small databases and partial expert knowledge are available.
Estimation of Pollution Solubility in Wastewater by Fusion of Expert Knowledge with Data using the Belief Functions Theory
, 2002
"... In this paper, we propose a methodology for combining expert knowledge with information extracted from statistical data, for estimating pollution solubility in wastewater. The method is based on (1) a casebased approach allowing to predict a quantity of interest from past cases in the form of a ..."
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In this paper, we propose a methodology for combining expert knowledge with information extracted from statistical data, for estimating pollution solubility in wastewater. The method is based on (1) a casebased approach allowing to predict a quantity of interest from past cases in the form of a belief function, (2) Bayesian networks for modelling expert knowledge and (3) a tuning mechanism allowing to mix information sources, so as to minimize a performance criterion. The use of this method for this environmental problem is motivated by the fact that knowledge in this domain is very partial and ill structured. The belief functions theory allows to handle the induced uncertainty and imprecision. The approach is expected to be useful in situations where both small databases and partial expert knowledge are available.
Building Probabilistic Networks: ªWhere Do the Numbers Come From?º Guest Editors ' Introduction
"... PROBABILISTIC networks are now fairly well established as ..."
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