Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification

Corani, Giorgio; Antonucci, Alessandro; Zaffalon, Marco

doi:10.1007/978-3-642-23166-7_4

Cited by 62 publications

(40 citation statements)

References 43 publications

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“…Additionally, some authors [10] have discussed the interest, in preference learning problems and in label ranking in particular, to predict partial orders rather than complete rankings. Such an approach can be seen as an extension of the reject option [2] or of the fact of making partial predictions [11]. Such cautious predictions can prevent harmful decisions based on incorrect predictions.…”

Section: Introductionmentioning

confidence: 99%

Cautious label ranking with label-wise decomposition

Destercke

Masson

Poss

2015

European Journal of Operational Research

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In this paper, we are interested in the label ranking problem. We are more specifically interested in the recent trend consisting in predicting partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a ranking method based on label-wise decomposition. We estimate an imprecise probabilistic model over each label rank and we infer a partial order from it using optimisation techniques. This leads to new results concerning a particular bilinear assignment problem. Finally, we provide some experiments showing the feasibility of our method.

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Section: Introductionmentioning

confidence: 99%

Cautious label ranking with label-wise decomposition

Destercke

Masson

Poss

2015

European Journal of Operational Research

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“…[6], I define an imprecise Bayes net as a triple I = V, E , K . As in a precise Bayes net, V is a set of variables and E is a acyclic set of directed edges.…”

Section: Imprecise Bayes Nets: the Basicsmentioning

confidence: 99%

“…Thus, it is an important question whether extensions of these models that allow for imprecise probabilities will yield similar fruit. Most work on imprecise Bayesian networks (IBNs) has occurred in the statistics and computer science literature (see [6] for a comprehensive overview) (In the computer science and statistical literature, IBNs are sometimes called credal networks). I avoid this term here because of the different usage of the term "credence" in formal epistemology.…”

Section: Introductionmentioning

confidence: 99%

Imprecise Bayesian Networks as Causal Models

Kinney

2018

Information

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This article considers the extent to which Bayesian networks with imprecise probabilities, which are used in statistics and computer science for predictive purposes, can be used to represent causal structure. It is argued that the adequacy conditions for causal representation in the precise context-the Causal Markov Condition and Minimality-do not readily translate into the imprecise context. Crucial to this argument is the fact that the independence relation between random variables can be understood in several different ways when the joint probability distribution over those variables is imprecise, none of which provides a compelling basis for the causal interpretation of imprecise Bayes nets. I conclude that there are serious limits to the use of imprecise Bayesian networks to represent causal structure.

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“…Such an approach can be seen as an extension of the reject option implemented in learning problems [3] or of the fact of making partial predictions [14]. Such cautious predictions can prevent harmful decisions based on incorrect predictions.…”

Section: Introductionmentioning

confidence: 99%

“…As making partial predictions is one central feature of imprecise probabilistic approaches [14], it seems interesting to investigate how one can use such approaches to predict partial orders. In addition, these approaches are also well-designed to cope with the problem of missing or incomplete data [33].…”

Section: Introductionmentioning

confidence: 99%

A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions

Destercke

2013

Advanced Information Systems Engineering

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Abstract. In this paper, we are interested in the label ranking problem. We are more specifically interested in the recent trend consisting in predicting partial but more accurate (i.e., making less incorrect statements) orders rather than complete ones. To do so, we propose a ranking method based on pairwise imprecise scores obtained from likelihood functions. We discuss how such imprecise scores can be aggregated to produce interval orders, which are specific types of partial orders. We then analyse the performances of the method as well as its sensitivity to missing data and parameter values.

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Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification

Cited by 62 publications

References 43 publications

Cautious label ranking with label-wise decomposition

Cautious label ranking with label-wise decomposition

Imprecise Bayesian Networks as Causal Models

A Pairwise Label Ranking Method with Imprecise Scores and Partial Predictions

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