Título

Inductive transfer for learning Bayesian networks

Autor

LUIS ENRIQUE SUCAR SUCCAR

EDUARDO FRANCISCO MORALES MANZANARES

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

Inductive transfer - (INDUCTIVE TRANSFER) Bayesian networks - (BAYESIAN NETWORKS) Structure learning - (STRUCTURE LEARNING) Parameter learning - (PARAMETER LEARNING) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)

Resumen o descripción

In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is equipment with similar specifications. In these cases it is common to have plenty of data for some scenarios but very little for others. In order to learn accurate models for rare cases, it is desirable to use data and knowledge from similar cases; a technique known as transfer learning. In this paper we propose an inductive transfer learning method for Bayesian networks, that considers both structure and parameter learning. For structure learning we use conditional independence tests, by combining measures from the target task with those obtained from one or more auxiliary tasks, using a novel weighted sum of the conditional independence measures. For parameter learning, we propose two variants of the linear pool for probability aggregation, combining the probability estimates from the target task with those from the auxiliary tasks. To validate our approach, we used three Bayesian networks models that are commonly used for evaluating learning techniques, and generated variants of each model by changing the structure as well as the parameters. We then learned one of the variants with a small dataset and combined it with information from the other variants. The experimental results show a significant improvement in terms of structure and parameters when we transfer knowledge from similar tasks. We also evaluated the method with real-world data from a manufacturing process considering several products, obtaining an improvement in terms of log-likelihood between the data and the model when we do transfer learning from related products.

Editor

Springer

Fecha de publicación

2009

Tipo de publicación

Artículo

Versión de la publicación

Versión aceptada

Recurso de información

http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1201

Formato

application/pdf

Idioma

Inglés

Audiencia

Estudiantes

Investigadores

Público en general

Sugerencia de citación

Roger L., et al., (2009). Inductive transfer for learning Bayesian networks, Machine Learning, (79): 227–255

Repositorio Orígen

Repositorio Institucional del INAOE

Descargas

262