Original title: Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions
Authors: Plajner, Martin ; Vomlel, Jiří
Document type: Papers
Conference/Event: Workshop on Uncertainty Processing (WUPES’18), Třeboň (CZ), 20180606
Year: 2018
Language: eng
Abstract: Learning parameters of a probabilistic model is a necessary step in most machine learning modeling tasks. When the model is complex and data volume is small the learning process may fail to provide good results. In this paper we present a method to improve learning results for small data sets by using additional information about the modelled system. This additional information is represented by monotonicity conditions which are restrictions on parameters of the model. Monotonicity simplifies the learning process and also these conditions are often required by the user of the system to hold. \n\nIn this paper we present a generalization of the previously used algorithm for parameter learning of Bayesian Networks under monotonicity conditions. This generalization allows both parents and children in the network to have multiple states. The algorithm is described in detail as well as monotonicity conditions are.\n\nThe presented algorithm is tested on two different data sets. Models are trained on differently sized data subsamples with the proposed method and the general EM algorithm. Learned models are then compared by their ability to fit data. We present empirical results showing the benefit of monotonicity conditions. The difference is especially significant when working with small data samples. The proposed method outperforms the EM algorithm for small sets and provides comparable results for larger sets.
Keywords: Bayesian networks; Learning model parameters; monotonicity condition
Project no.: GA16-12010S (CEP), SGS17/198/OHK4/3T/14 (CEP)
Funding provider: GA ČR, ČVUT
Host item entry: Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18), ISBN 978-80-7378-361-7

Institution: Institute of Information Theory and Automation AS ČR (web)
Document availability information: Fulltext is available at external website.
External URL: http://library.utia.cas.cz/separaty/2018/MTR/plajner-0490309.pdf
Original record: http://hdl.handle.net/11104/0284592

Permalink: http://www.nusl.cz/ntk/nusl-375672

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Research > Institutes ASCR > Institute of Information Theory and Automation
Conference materials > Papers
 Record created 2018-06-19, last modified 2019-10-20

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