[1]汤礼颖,贺利乐,何林,等.一种卷积神经网络集成的多样性度量方法[J].智能系统学报,2021,16(6):1030-1038.[doi:10.11992/tis.202011023]
 TANG Liying,HE Lile,HE Lin,et al.Diversity measuring method of a convolutional neural network ensemble[J].CAAI Transactions on Intelligent Systems,2021,16(6):1030-1038.[doi:10.11992/tis.202011023]
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一种卷积神经网络集成的多样性度量方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年6期
页码:
1030-1038
栏目:
学术论文—机器感知与模式识别
出版日期:
2021-11-05

文章信息/Info

Title:
Diversity measuring method of a convolutional neural network ensemble
作者:
汤礼颖1 贺利乐1 何林2 屈东东1
1. 西安建筑科技大学 机电工程学院, 陕西 西安 710055;
2. 西安建筑科技大学 理学院, 陕西 西安 710055
Author(s):
TANG Liying1 HE Lile1 HE Lin2 QU Dongdong1
1. School of Mechanical and Electrical Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China;
2. School of Science, Xi ’an University of Architecture and Technology, Xi’an 710055, China
关键词:
卷积神经网络集成学习多样性度量机器学习分类器集成概率向量输出Oracle输出基模型
Keywords:
CNNensemble learningdiversity measuresmachine learningmultiple classifier ensemblesprobability vector outputsOracle outputsbasic model
分类号:
TP181;TP391
DOI:
10.11992/tis.202011023
摘要:
分类器模型之间的多样性是分类器集成的一个重要性能指标。目前大多数多样性度量方法都是基于基分类器模型的0/1输出结果(即Oracle 输出)进行计算,针对卷积神经网络的概率向量输出结果,仍需要将其转化为Oracle输出方式进行度量,这种方式未能充分利用卷积神经网络输出的概率向量所包含的丰富信息。针对此问题,利用多分类卷积神经网络模型的输出特性,提出了一种基于卷积神经网络的概率向量输出方式的集成多样性度量方法,建立多个不同结构的卷积神经网络基模型并在CIFAR-10和CIFAR-100数据集上进行实验。实验结果表明,与双错度量、不一致性度量和Q统计多样性度量方法相比,所提出的方法能够更好地体现模型之间的多样性,为模型选择集成提供更好的指导。
Abstract:
Diversity among classifier models has been recognized as a significant performance index of a classifier ensemble. Currently, most diversity measuring methods are defined based on the 0/1 outputs (namely Oracle outputs) of the base model. The probability vector outputs of a convolutional neural network (CNN) still need to be converted into Oracle outputs for measurement, which fails to fully use the rich information contained in the CNN probability vector outputs. To solve this problem, a new diversity measuring method for probabilistic vector outputs based on CNNs is proposed. Several base models of CNN models with various structures are established and tested on the CIFAR-10 and CIFAR-100 datasets. Compared with double-fault measure, disagreement measure, and Q-Statistic, the proposed method can better reflect the differences between the models and provide better guidance for a selective ensemble of CNN models.

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备注/Memo

备注/Memo:
收稿日期:2020-11-20。
基金项目:国家自然科学基金项目(61903291)
作者简介:汤礼颖,硕士研究生,主要研究方向为图像识别与目标检测;贺利乐,教授,博士生导师,主要研究方向为机器人智能化技术、机器学习。2015年获陕西省高等学校科学技术奖二等奖,2016年获陕西省科学技术奖三等奖。获发明专利授权5件,出版专著1部,教材4部,发表学术论文86篇;何林,讲师,主要研究方向为深度学习
通讯作者:贺利乐.E-mail:hllnh2013@163.com
更新日期/Last Update: 2021-12-25