基于拟合型弱分类器的AdaBoost算法

doi:10.11959/j.issn.1000-0801.2019219

Abstract

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%.

Key words: AdaBoost, fitting type, least squares, weak classifier

CLC Number:

TP391.4

Pengfeng SONG,Qingwei YE,Zhihua LU,Yu ZHOU. AdaBoost algorithm based on fitted weak classifier[J]. Telecommunications Science, 2019, 35(11): 27-35.

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References 13

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数据集	实例数	特征维数
Sonar	208	60
CMC	962	9
Mushroom	5 644	22
Credit-g	1 000	20
Cylinder-bands	277	39

对比项	Discrete-AdaBoost	Real-AdaBoost	线性拟合	二阶拟合	三阶拟合	迭代次数
Sonar	0.116	0.113	0.121	0.105	0.079	30
	0.097	0.058	0.050	0.052	0.037	60
	0.042	0.026	0.024	0.016	0.005	100
CMC	0.413	0.391	0.386	0.381	0.364	30
	0.401	0.383	0.377	0.366	0.351	60
	0.390	0.371	0.368	0.357	0.326	100
Mushroom	0.328	0.331	0.320	0.321	0.315	30
	0.322	0.325	0.315	0.311	0.307	60
	0.315	0.313	0.309	0.302	0.298	100
Credit-g	0.211	0.205	0.186	0.188	0.186	30
	0.208	0.201	0.191	0.171	0.175	60
	0.199	0.193	0.176	0.161	0.160	100
Cylinder-bands	0.144	0.158	0.140	0.081	0.077	30
	0.112	0.117	0.094.	0.050	0.031	60
	0.103	0.108	0.090	0.036	0.027	100

对比项	Discrete-AdaBoost/s	Real-AdaBoost/s	线性拟合/s	二阶拟合/s	三阶拟合/s
Sonar	148	133	19	32	41
CMC	266	251	23	34	53
Mushroom	354	369	32	67	81
Credit-g	276	280	27	45	70
Cylinder-bands	136	127	12	21	43

对比项	Discrete-AdaBoost	Real-AdaBoost	本文算法
人脸数/张	159	159	159
检出人脸/张	133	137	139
漏检个数/张	26	25	20
误检个数/张	51	57	34
检测率	83.64%	86.16%	87.42%
误检率	24.29%	26.39%	17.62%

对比项	Discrete-AdaBoost	Real-AdaBoost	本文算法
人脸数/张	205	205	205
检出人脸/张	192	197	198
漏检个数/张	13	8	7
误检个数/张	18	13	9
检测率	93.66%	96.09%	96.59%
误检率	8.07%	5.96%	4.21%

AdaBoost algorithm based on fitted weak classifier

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