Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F18%3A00490309" target="_blank" >RIV/67985556:_____/18:00490309 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Gradient Descent Parameter Learning of Bayesian Networks under Monotonicity Restrictions

Original language description

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. nnIn 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.nnThe 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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA16-12010S" target="_blank" >GA16-12010S: Conditional independence structures: combinatorial and optimization methods</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the 11th Workshop on Uncertainty Processing (WUPES’18)

ISBN

978-80-7378-361-7

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

153-164

Publisher name

MatfyzPress, Publishing House of the Faculty of Mathematics and Physics Charles University

Place of publication

Praha

Event location

Třeboň

Event date

Jun 6, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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