An Improved Dung Beetle Optimizer for Random Forest Optimization
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 algorithm, followed by SHapley Additive exPlanations (SHAP) analysis.
Code (0)
등록된 구현이 없습니다.
Tasks
Binary ClassificationDiversityPositionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Memory Enhanced Fractional-Order Dung Beetle Optimization for Photovoltaic Parameter Identification
Accurate parameter identification in photovoltaic (PV) models is crucial for performance evaluation but remains challenging due to their nonlinear, multimodal, and high-dimensional nature. Although the Dung Beetle Optimi…
Bombardier Beetle Optimizer: A Novel Bio-Inspired Algorithm for Global Optimization
In this paper, a novel bio-inspired optimization algorithm is proposed, called Bombardier Beetle Optimizer (BBO). This type of species is very intelligent, which has an ability to defense and escape from predators. The p…
Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning
Bark beetle outbreaks can dramatically impact forest ecosystems and services around the world. For the development of effective forest policies and management plans, the early detection of infested trees is essential. De…
Data AugmentationDeep LearningManagementBSAS: Beetle Swarm Antennae Search Algorithm for Optimization Problems
Beetle antennae search (BAS) is an efficient meta-heuristic algorithm. However, the convergent results of BAS rely heavily on the random beetle direction in every iterations. More specifically, different random seeds may…
PositionDetection of Bark Beetle Attacks using Hyperspectral PRISMA Data and Few-Shot Learning
Bark beetle infestations represent a serious challenge for maintaining the health of coniferous forests. This paper proposes a few-shot learning approach leveraging contrastive learning to detect bark beetle infestations…
Contrastive LearningFew-Shot Learning