AdaBoost algorithm based on fitted weak classifier

Pengfeng SONG; Qingwei YE; Zhihua LU; Yu ZHOU

doi:10.11959/j.issn.1000-0801.2019219

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AdaBoost algorithm based on fitted weak classifier

research and development | 更新时间：2024-06-05

- AdaBoost algorithm based on fitted weak classifier
- Telecommunications Science Vol. 35, Issue 11, Pages: 27-35(2019)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.1000-0801.2019219
  CLC： TP391.4
- Published Online：2019-11，
  
  Published：20 November 2019
- 稿件说明：
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Pengfeng SONG, Qingwei YE, Zhihua LU, et al. AdaBoost algorithm based on fitted weak classifier[J]. Telecommunications science, 2019, 35(11): 27-35.
DOI：

Pengfeng SONG, Qingwei YE, Zhihua LU, et al. AdaBoost algorithm based on fitted weak classifier[J]. Telecommunications science, 2019, 35(11): 27-35. DOI： 10.11959/j.issn.1000-0801.2019219.

摘要

针对AdaBoost算法通过最小化训练错误率来选择弱分类器造成的精度不佳问题以及单阈值作为弱分类器训练过程较慢难以收敛问题，提出了一种基于拟合型弱分类器的AdaBoost算法。首先针对每个特征，在特征值与标记值之间建立映射关系，引入最小二乘法求解拟合多项式函数，并转换成离散分类值，从而获得弱分类器。其次从获得的众多弱分类器中，选择分类误差最小的弱分类器作为本轮迭代的最佳弱分类器，构成新的 AdaBoost 强分类器。与传统训练算法相比，极大地减少了待选弱分类器的个数。选取 UCI 数据集和MIT人脸图像数据库进行实验验证，相较于传统Discrete-AdaBoost算法，改进算法的训练速度提升了一个数量级，人脸检测率可达96.59%。

Abstract

AdaBoost algorithm was proposed to minimize the accuracy caused by weak classifiers by minimizing the training error rate

and the single threshold was weaker and difficult to converge.The AdaBoost algorithm based on the fitted weak classifier was proposed.Firstly

the mapping relationship between eigenvalues and marker values was established.The least squares method was introduced to solve the fitting polynomial function

and the continuous fitting values were converted into discrete categorical values

thereby obtaining a weak classifier.From the many classifiers obtained

the classifier with smaller fitting error was selected as the weak classifier to form a new AdaBoost strong classifier.The UCI dataset and the MIT face image database were selected for experimental verification.Compared with the traditional Discrete-AdaBoost algorithm

the training speed of the improved algorithm was increased by an order of magnitude.And the face detection rate can reach 96.59%.

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