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论文:2017,Vol:35,Issue(6):1119-1124 |
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引用本文： |
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张苗燕, 王登飞, 魏宗寿. 一种改进的AdaBoost快速训练算法[J]. 西北工业大学学报 |
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Zhang Miaoyan, Wang Dengfei, Wei Zongshou. An Improved AdaBoost Training Algorithm[J]. Northwestern polytechnical university |
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| 一种改进的AdaBoost快速训练算法 |
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| 张苗燕1,2, 王登飞1,2, 魏宗寿1,2 |
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1. 兰州交通大学 自动控制研究所, 甘肃 兰州 730070;
2. 甘肃省高原交通信息工程及控制重点实验室, 甘肃 兰州 730070 |
| 摘要: |
| 针对AdaBoost算法误检率及收敛速度问题，结合改进的细菌觅食优化算法的思想，提出一种基于改进细菌觅食的AdaBoost弱分类器优化权重算法。采用改进的随机化佳点集方法构造初始种群，改进的趋化策略、变次数游动策略及变概率迁徙策略来全局优化搜索最佳弱分类器。对最佳弱分类器的加权系数作以改进，其加权系数不仅与错误率有关，也应与对正样本的识别能力及弱分类器的可靠性有关。选取4种UCI数据集进行实验验证，基于Matlab的仿真结果表明，改进方法获得了较好的检测性能。 |
| 关键词:
AdaBoost
细菌觅食优化算法
随机化佳点集
弱分类器
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| An Improved AdaBoost Training Algorithm |
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| Zhang Miaoyan1,2, Wang Dengfei1,2, Wei Zongshou1,2 |
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1. Automatic Control Research Institute Lanzhou Jiaotong University, Lanzhou 730070, China;
2. Gansu Provincial Key Laboratory of Traffic Information Engineering and Control, Lanzhou 730070, China |
| Abstract: |
| Aimed at the problem of mis-decetion rate and the convergence speed, and combined with improved Bacterial foraging optimization algorithm, this paper presented an improved AdaBoost algorithm named optimal weighting algorithm of weak classifiers based improved BF-based AdaBoost. In this paper, adopted an reformative good point set based randomization method to construct the initial population, and used some strategies such as improved chemotaxis direction policies, variable frequency winding tactics and changing probability of migration operations to soulord the weak classifiers. In order to modify the weight coefficients of optimal weak classifiers, the weighting coefficient was not only related to the error rates, but also the recognition of positive samples and the reliability of classifiers. Experiment results of simulation by selecting four UCI data sets based on MATLAB indicate the improved method obtains better detection performance than traditional AdaBoost algorithm. |
| Key words:
AdaBoost
bacterial foraging optimization algorithm
the randomization good point set
weak classifier
design of experiments
MATLAB
particle swarm optimization(PSO)
support vector machines
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| 收稿日期: 2016-12-08
修回日期:
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| DOI: |
| 基金项目: 甘肃省基础研究创新群体计划（1606RJIA327）、陇原青年创新人才扶持计划（2016-38）、甘肃省科技支撑计划（1604GKCA009）与兰州交通大学校青年基金（2016024）资助 |
| 通讯作者:
Email： |
| 作者简介: 张苗燕(1993-),女,兰州交通大学硕士研究生,主要从事数字图像处理研究。
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