江苏大学学报（自然科学版）2026，Vol.47Issue(3)：308-315,328,9.DOI:10.3969/j.issn.1671-7775.2026.03.008
基于自适应惩罚和多目标粒子群优化的AdaBoost方法
AdaBoost method based on adaptive penalty and multi-objective particle swarm optimization
摘要
Abstract
To solve the issue of noise samples causing bias in AdaBoost training and the difficulty of achieving stable pruning results with single-objective particle swarm optimization for pruning ensemble models,the AdaBoost method was proposed based on the adaptive penalty and multi-objective particle swarm optimization.The adaptive penalty strategy was proposed to apply weighted penalties to suspicious noise for reducing impact on the subsequent training process.The two-stage multi-strategy multi-objective particle swarm optimization algorithm(TSMSMOPSO)was introduced for ensemble pruning.During the search phase,the particles were accelerated toward non-dominated particles to avoid searching worthless space.The global optimum was selected by considering the trade-off between diversity and convergence.To prevent getting stuck in local optima,the reference particles were randomly mutated to generate elite particles for enhancing population diversity in the exploitation phase.To validate the performance of the proposed algorithm,seven comparison algorithms were evaluated on 16 datasets.The adaptive penalty strategy and TSMSMOPSO were verified through the ablation tests and the pruning comparison experiment.The results show that the proposed algorithm achieves the highest accuracy on 12 datasets and the best F,score on 13 datasets,where the differences from the suboptimal values are 0.19％-16.67％and 0.62％-16.67％,respectively.Compared to single-objective particle swarm optimization,the pruned ensemble model of TSMSMOPSO is lighter in ensemble size and exhibits more stable pruning effects.关键词
AdaBoost/集成学习/多目标优化/噪声惩罚/粒子群优化/集成剪枝Key words
AdaBoost/ensemble learning/multi-objective optimization/noise penalty/particle swarm optimization/ensemble pruning分类
信息技术与安全科学引用本文复制引用
韩飞,葛钰彬..基于自适应惩罚和多目标粒子群优化的AdaBoost方法[J].江苏大学学报（自然科学版）,2026,47(3):308-315,328,9.基金项目
国家自然科学基金资助项目(61976108,61572241) （61976108,61572241）
