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| HAL: hal-00585564, version 1 |
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| 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, Belfast : Irlande (2011) |
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| Mixture of Markov trees for Bayesian network structure learning with small atasets in high dimensional space |
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| Sourour Ammar 1Philippe Leray 1 |
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| (2011-06-30) |
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| The recent explosion of high dimensionality in datasets for several domains has posed a serious challenge to existing Bayesian network structure learning algorithms. Local search methods represent a solution in such spaces but suffer with small datasets. MMHC (Max-Min Hill-Climbing) is one of these local search algorithms where a first phase aims at identifying a possible skeleton by using some statistical association measurements and a second phase performs a greedy search restricted by this skeleton. We propose to replace the first phase, imprecise when the number of data remains relatively very small, by an application of "Perturb and Combine" framework we have already studied in density estimation by using mixtures of bagged trees. |
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| 1: | Laboratoire d'Informatique de Nantes Atlantique (LINA) |
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CNRS : UMR6241 – Université de Nantes – École Nationale Supérieure des Mines - Nantes |
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| Subject | : | Cognitive science/Computer science |
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| Bayesian networks – mixture of trees – bootstrap – structure learning |
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| hal-00585564, version 1 | |
| http://hal.archives-ouvertes.fr/hal-00585564 | |
| oai:hal.archives-ouvertes.fr:hal-00585564 | |
| From: Sourour Ammar | |
| Submitted on: Wednesday, 13 April 2011 12:18:53 | |
| Updated on: Tuesday, 4 December 2012 11:07:14 | |
