<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Clarifying the features of the packaging of cultural and creative products is beneficial to out-of-the-loop cultural and creative products. In this paper, starting from probability theory, graph theory and information theory principles, we combine Bayesian network structure and feature selection theory to construct a feature extraction model for cultural and creative product packaging. The maximum mutual information principle is used to optimize the feature selection scoring function, and experimental analyses of execution time and performance comparisons are conducted for the BNCC-FS method. In terms of execution time, the average training time of the BNCC-FS method on the eight datasets is 4.73s, which is 14.53% and 43.44% lower compared to CC-FS and ECC-FS, respectively. In terms of performance comparison, the average HammingLoss value of the BNCC-FS method on sixteen datasets was 0.155, which was 2.52% and 10.41% lower compared to CC-FS and ECC-FS, respectively. This shows that the Bayesian network-based feature extraction method can effectively achieve feature extraction of the data and also provides a new research database for the analysis of the packaging features of cultural and creative products.</p>
</abstract>

Clarifying the features of the packaging of cultural and creative products is beneficial to out-of-the-loop cultural and creative products. In this paper, starting from probability theory, graph theory and information theory principles, we combine Bayesian network structure and feature selection theory to construct a feature extraction model for cultural and creative product packaging. The maximum mutual information principle is used to optimize the feature selection scoring function, and experimental analyses of execution time and performance comparisons are conducted for the BNCC-FS method. In terms of execution time, the average training time of the BNCC-FS method on the eight datasets is 4.73s, which is 14.53% and 43.44% lower compared to CC-FS and ECC-FS, respectively. In terms of performance comparison, the average HammingLoss value of the BNCC-FS method on sixteen datasets was 0.155, which was 2.52% and 10.41% lower compared to CC-FS and ECC-FS, respectively. This shows that the Bayesian network-based feature extraction method can effectively achieve feature extraction of the data and also provides a new research database for the analysis of the packaging features of cultural and creative products.

Analysis of the packaging characteristics of creative products in the context of new media based on Bayesian networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Clarifying the features of the packaging of cultural and creative products is beneficial to out-of-the-loop cultural and creative products. In this paper,...

Analysis of the packaging characteristics of creative products in the context of new media based on Bayesian networks

Published Online: Sep 25, 2023

Page range: -

Received: Dec 08, 2022

Accepted: Apr 05, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00371

Keywords
Bayesian networks, Feature processing theory, BNCC-FS method, Feature analysis, Packaging of cultural and creative products.

© 2023 Lingyun Xie et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Analysis of the packaging characteristics of creative products in the context of new media based on Bayesian networks

Published Online: Sep 25, 2023

Page range: -

Received: Dec 08, 2022

Accepted: Apr 05, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00371

KeywordsBayesian networks, Feature processing theory, BNCC-FS method, Feature analysis, Packaging of cultural and creative products.

© 2023 Lingyun Xie et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Bayesian networks, Feature processing theory, BNCC-FS method, Feature analysis, Packaging of cultural and creative products.