Accuracy comparison between decision tree and naive Bayes algorithms in large discrete dataset with incompletely independent attributes

Hao Li; Yu Wan; Junbo Yang

doi:10.1117/12.3004158

11 October 2023 Accuracy comparison between decision tree and naive Bayes algorithms in large discrete dataset with incompletely independent attributes

Hao Li, Yu Wan, Junbo Yang

Author Affiliations +

Proceedings Volume 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023); 128000D (2023) https://doi.org/10.1117/12.3004158
Event: 6th International Conference on Computer Information Science and Application Technology (CISAT 2023), 2023, Hangzhou, China

Abstract

Decision tree and naive bayes algorithms as classification algorithms are largely implemented in data mining. They are widely used in various data types which present accuracy differences among each type. In this essay, principles of two algorithms are introduced, and a specific data type, a large discrete dataset with incompletely independent attributes is used as a model to train those two algorithms. Then, two algorithms are tested, and confusion matrix method is used to calculate the accuracy of algorithms. Finally, two algorithms are compared, and it is concluded that accuracy of decision trees is better with large discrete datasets and attribute correlation.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hao Li, Yu Wan, and Junbo Yang "Accuracy comparison between decision tree and naive Bayes algorithms in large discrete dataset with incompletely independent attributes", Proc. SPIE 12800, Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023), 128000D (11 October 2023); https://doi.org/10.1117/12.3004158

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