An Improved Dung Beetle Optimizer for Random Forest Optimization
Lianghao Tan, Xiaoyi Liu, Dong Liu, Shubing Liu, Weixi Wu, Huangqi, Jiang

TL;DR
This paper introduces an enhanced Dung Beetle Optimizer with circle mapping and crossover strategies, significantly improving convergence and accuracy in hyperparameter tuning for Random Forest classifiers, validated on benchmarks and retail data.
Contribution
The paper presents a novel improved Dung Beetle Optimizer (CICRDBO) that enhances global search and convergence speed for hyperparameter optimization.
Findings
Improved algorithm outperforms standard DBO in benchmark tests.
Enhanced hyperparameter tuning leads to better Random Forest accuracy.
SHAP analysis confirms model interpretability.
Abstract
To improve the convergence speed and optimization accuracy of the Dung Beetle Optimizer (DBO), this paper proposes an improved algorithm based on circle mapping and longitudinal-horizontal crossover strategy (CICRDBO). First, the Circle method is used to map the initial population to increase diversity. Second, the longitudinal-horizontal crossover strategy is applied to enhance the global search ability by ensuring the position updates of the dung beetle. Simulations were conducted on 10 benchmark test functions, and the results demonstrate that the improved algorithm performs well in both convergence speed and optimization accuracy. The improved algorithm is further applied to the hyperparameter selection of the Random Forest classification algorithm for binary classification prediction in the retail industry. Various combination comparisons prove the practicality of the improved…
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Taxonomy
TopicsRemote Sensing and LiDAR Applications · Engineering Applied Research
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
