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2023 | OriginalPaper | Buchkapitel

Application of Decision Tree and Bayesian Classification Algorithm in Visual Communication Design

verfasst von : Jing Bai

Erschienen in: Frontier Computing

Verlag: Springer Nature Singapore

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Abstract

The application of decision tree and Bayesian classification algorithm in visual communication design is to find the best solution or model for a given problem. The decision tree is used to classify the project into one of the predefined classes according to the characteristics of the project. It helps to find out which function has the maximum, minimum or average value among all other functions. The output of this model can be used as the input of Bayesian classifier so that it can provide a good prediction of future values based on past data. The application in visual communication design is the process of using decision tree to help you decide which elements should be included in the design. The decision tree is used to determine whether the user can complete an action (such as clicking an element) without encountering any problems.

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Literatur
1.
Zurück zum Zitat Zhang, X., Wang, M.: Weighted random forest algorithm based on Bayesian algorithm. In: Journal of Physics: Conference Series, vol. 1924, no. 1, p. 012006 (2021) Zhang, X., Wang, M.: Weighted random forest algorithm based on Bayesian algorithm. In: Journal of Physics: Conference Series, vol. 1924, no. 1, p. 012006 (2021)
2.
Zurück zum Zitat Chavez, T., Vohra, N., Bailey, K., et al.: Supervised Bayesian learning for breast cancer detection in terahertz imaging. Biomed. Signal Process. Control 70, 102949 (2021)CrossRef Chavez, T., Vohra, N., Bailey, K., et al.: Supervised Bayesian learning for breast cancer detection in terahertz imaging. Biomed. Signal Process. Control 70, 102949 (2021)CrossRef
3.
Zurück zum Zitat Alotaibi, A., Hamdaoui, R.: Improvement of semi-supervised document classification based on fine tuning naive Bayesian classifier. Eur. J. Sci. Res 158(3), 181–190 (2021) Alotaibi, A., Hamdaoui, R.: Improvement of semi-supervised document classification based on fine tuning naive Bayesian classifier. Eur. J. Sci. Res 158(3), 181–190 (2021)
4.
Zurück zum Zitat Levi, R., Valderhaug, V.D., Castelbuono, S., et al.: Bayesian supervised machine learning classification of neural networks with pathological perturbations. Biomed. Phys. Eng. Express 7(6), 065021 (2021)CrossRef Levi, R., Valderhaug, V.D., Castelbuono, S., et al.: Bayesian supervised machine learning classification of neural networks with pathological perturbations. Biomed. Phys. Eng. Express 7(6), 065021 (2021)CrossRef
5.
Zurück zum Zitat Liu, Y., Jiang, D., Duan, H., et al.: Dynamic gesture recognition algorithm based on 3D convolutional neural network. Comput. Intell. Neurosci. 2021(12), 1–12 (2021) Liu, Y., Jiang, D., Duan, H., et al.: Dynamic gesture recognition algorithm based on 3D convolutional neural network. Comput. Intell. Neurosci. 2021(12), 1–12 (2021)
6.
Zurück zum Zitat Neiswanger, W., Wang, K.A., Ermon, S.: Bayesian algorithm execution: estimating computable properties of black-box functions using mutual information (2021) Neiswanger, W., Wang, K.A., Ermon, S.: Bayesian algorithm execution: estimating computable properties of black-box functions using mutual information (2021)
7.
Zurück zum Zitat Zhang, C., Storlie, C.B., Lee, T.: Sensitivity analysis in classification using Bayesian smoothing spline ANOVA probit regression. Can. J. Stat. 50, 928–950 (2021)MathSciNetCrossRef Zhang, C., Storlie, C.B., Lee, T.: Sensitivity analysis in classification using Bayesian smoothing spline ANOVA probit regression. Can. J. Stat. 50, 928–950 (2021)MathSciNetCrossRef
8.
Zurück zum Zitat Brandes, S., Sicks, F., Berger, A.: Behaviour Classification on giraffes (giraffa camelopardalis) using machine learning algorithms on triaxial acceleration data of two commonly used GPS devices and its possible application for their management and conservation (2021) Brandes, S., Sicks, F., Berger, A.: Behaviour Classification on giraffes (giraffa camelopardalis) using machine learning algorithms on triaxial acceleration data of two commonly used GPS devices and its possible application for their management and conservation (2021)
9.
Zurück zum Zitat Liu, Y., Dou, Y., Jin, R., et al.: Hierarchical learning with backtracking algorithm based on the visual confusion label tree for large-scale image classification. Vis. Comput. 38, 897–917 (2021)CrossRef Liu, Y., Dou, Y., Jin, R., et al.: Hierarchical learning with backtracking algorithm based on the visual confusion label tree for large-scale image classification. Vis. Comput. 38, 897–917 (2021)CrossRef
10.
Zurück zum Zitat Liwei, Z.: Predictive analysis of machine learning error classification based on bayesian network. Wirel. Pers. Commun. 127, 1–20 (2021) Liwei, Z.: Predictive analysis of machine learning error classification based on bayesian network. Wirel. Pers. Commun. 127, 1–20 (2021)
Metadaten
Titel
Application of Decision Tree and Bayesian Classification Algorithm in Visual Communication Design
verfasst von
Jing Bai
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-1428-9_254

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