Flexible brain: a domain-model based bayesian network for classification

Currently, deep learning methods have been widely applied to many fields like classification. Generally, these methods use the technology like transferring to make a model work well on different domains like building a strong brain. Existing transferring methods include complex model reconstruction...

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Bibliographic Details
Published inJournal of experimental & theoretical artificial intelligence Vol. 34; no. 6; pp. 1011 - 1028
Main Authors Jin, Guanghao, Song, Qingzeng
Format Journal Article
LanguageEnglish
Published Abingdon Taylor & Francis 02.11.2022
Taylor & Francis Ltd
Subjects
Bayesian analysis
Bayesian network
Brain
Classification
Deep learning
Domains
flexible brain
Machine learning
transferring
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Summary:Currently, deep learning methods have been widely applied to many fields like classification. Generally, these methods use the technology like transferring to make a model work well on different domains like building a strong brain. Existing transferring methods include complex model reconstruction or high-quality retraining on the new domains that makes it hard to implement or ensure high accuracy. This paper introduces a domain-model-based Bayesian network and related solutions to solve this problem. Our solutions make it easier to add new domains while ensure high accuracy like a flexible brain. The experimental results show that our solutions can ensure higher accuracy than the single model one. Furthermore, we also evaluated the network in transferring case and the result shows that the accuracy of our solutions is higher than the single transferred model.
ISSN:0952-813X
1362-3079
DOI:10.1080/0952813X.2021.1949753