Decision tree prediction based on preceding feature engineering

Guoli Xie; Lei Yang; Sijin Li; Jiacheng Lai

doi:10.1117/12.3042135

17 October 2024 Decision tree prediction based on preceding feature engineering

Guoli Xie, Lei Yang, Sijin Li, Jiacheng Lai

Author Affiliations +

Proceedings Volume 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024); 1328911 (2024) https://doi.org/10.1117/12.3042135
Event: The International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 2024, Hangzhou, China

Abstract

This research employed feature engineering techniques to preprocess an original stock dataset, followed by the introduction of a decision tree prediction model for forecasting the dataset. Experimental results demonstrate an enhancement in predictive performance, offering a more effective analytical tool for forecasting stock market trends. This approach also serves as an inspiration in fields such as optoelectronic signal processing, optical image recognition, and optical computation and processing.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guoli Xie, Lei Yang, Sijin Li, and Jiacheng Lai "Decision tree prediction based on preceding feature engineering", Proc. SPIE 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 1328911 (17 October 2024); https://doi.org/10.1117/12.3042135

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