计算机工程 ›› 2012, Vol. 38 ›› Issue (2): 178-180.doi: 10.3969/j.issn.1000-3428.2012.02.058

• 人工智能及识别技术 • 上一篇    下一篇

快速多分类器集成算法研究

张伟松,高智英   

  1. (中国科学技术大学电子科学与技术系,合肥 230027)
  • 收稿日期:2011-04-11 出版日期:2012-01-20 发布日期:2012-01-20
  • 作者简介:张伟松(1984-),男,硕士研究生,主研方向:智能信息处理,模式识别,图像处理;高智英,硕士研究生
  • 基金项目:
    国家自然科学基金-广东省联合基金资助重点项目(U083 5002)

Research on Fast Multi-classifier Ensemble Algorithm

ZHANG Wei-song, GAO Zhi-ying   

  1. (Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China)
  • Received:2011-04-11 Online:2012-01-20 Published:2012-01-20

摘要: 研究快速多分类器集成算法。对多分类器集成需选定一定数量的弱分类器,再为每个弱分类器分配一定权重。在选择弱分类器时,通过计算每个弱分类器在全部训练样本集上的分类错误率,对其进行排序,挑选出分类效果最好的若干弱分类器。在多分类器权重分配策略上,提出2种权重分配方法:Biased AdaBoost算法与基于差分演化的多分类器集成算法。在人脸数据库上的实验结果表明,与经典AdaBoost算法相比,该算法能有效降低训练时间,提高识别准确率。

关键词: 快速多分类器集成, 差分演化, AdaBoost算法, 人脸识别, 训练时间

Abstract: This paper presents the research on fast Multi-classifier Ensemble(MCE) algorithm. MCE gets a number of classifiers, and assigns weights to the classifiers. A certain number of best classifiers can be gotten based on the error rate of every classifier. Assigning the weight of classifier is researched on, and two training methods are presented. The first is Biased AdaBoost algorithm which is sequentially to compute the weight of classifier. The second is DE-MCE based on Differential Evolution(DE) algorithm which optimizes the weights of all selected classifiers. Experimental result on face recognition shows that the training time of the algorithm is better than AdaBoost algorithm, and has high accuracy rate.

Key words: fast Multi-classifier Ensemble(MCE), Differential Evolution(DE), AdaBoost algorithm, face recognition, training time

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