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[1]余鹰,王乐为,吴新念,等.基于改进卷积神经网络的多标记分类算法[J].智能系统学报,2019,14(03):566-574.[doi:10.11992/tis.201804056]
 YU Ying,WANG Lewei,WU Xinnian,et al.A multi-label classification algorithm based on an improved convolutional neural network[J].CAAI Transactions on Intelligent Systems,2019,14(03):566-574.[doi:10.11992/tis.201804056]
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基于改进卷积神经网络的多标记分类算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第14卷
期数:
2019年03期
页码:
566-574
栏目:
出版日期:
2019-05-05

文章信息/Info

Title:
A multi-label classification algorithm based on an improved convolutional neural network
作者:
余鹰1 王乐为1 吴新念1 伍国华2 张远健3
1. 华东交通大学 软件学院, 江西 南昌 330013;
2. 中南大学 交通运输工程学院, 湖南 长沙 410000;
3. 同济大学计算机科学与技术系, 上海 201804
Author(s):
YU Ying1 WANG Lewei1 WU Xinnian1 WU Guohua2 ZHANG Yuanjian3
1. College of Software Engineering, East China Jiaotong University, Nanchang 330013, China;
2. College of Transportation Engineering, Central South University, Changsha 410000, China;
3. Department of Computer Science and Technology, Tongji Universi
关键词:
多标记学习卷积神经网络迁移学习全连接层特征表达多标记分类深度学习损失函数
Keywords:
multi-label learningconvolutional neural networktransfer learningfully-connected layerfeature expressionmulti-label classificationdeep learningloss function
分类号:
TP181
DOI:
10.11992/tis.201804056
摘要:
良好的特征表达是提高模型性能的关键,然而当前在多标记学习领域,特征表达依然采用人工设计的方式,所提取的特征抽象程度不高,包含的可区分性信息不足。针对此问题,提出了基于卷积神经网络的多标记分类模型ML_DCCNN,该模型利用卷积神经网络强大的特征提取能力,自动学习能刻画数据本质的特征。为了解决深度卷积神经网络预测精度高,但训练时间复杂度不低的问题,ML_DCCNN利用迁移学习方法缩减模型的训练时间,同时改进卷积神经网络的全连接层,提出双通道神经元,减少全连接层的参数量。实验表明,与传统的多标记分类算法以及已有的基于深度学习的多标记分类模型相比,ML_DCCNN保持了较高的分类精度并有效地提高了分类效率,具有一定的理论与实际价值。
Abstract:
A good feature expression is the key to improve model performance. However, at present, artificially designed features are used for multi-label learning. Thus, the level of abstraction of the extracted features is low and lacks the discriminated information involved. To solve this problem, this paper proposes a multi-label classification model based on convolutional neural network (ML_DCCNN). This model uses the powerful feature extraction capabilities of CNNs to automatically learn the features from the data. To solve the problem of high forecasting precision versus long training time of CNNs, the ML_DCCNN uses the transfer learning method to reduce the training time of the model. In addition, the entire connection layer of the CNN is improved by a dual-channel neuron, which can reduce the number of parameters of the fully connected layer. The experiments show that compared with the traditional multi-label classification algorithm and existing multi-label classification model based on deep learning, the ML_DCCNN maintains high classification accuracy and can effectively improve the classification efficiency, presenting certain theoretical and practical value.

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备注/Memo

备注/Memo:
收稿日期:2018-04-26。
基金项目:国家自然科学基金项目(61563016,61603404,61462037,61663002);江西省教育厅科技项目(GJJ150546);江西省自然科学基金项目(2018BAB202023).
作者简介:余鹰,女,1979年生,副教授,博士,主要研究方向为多标记学习、计算机视觉、粒计算;王乐为,男,1993年生,硕士研究生,主要研究方向为计算机视觉、深度学习;吴新念,女,1993年生,硕士研究生,主要研究方向为多标记学习、粒计算。
通讯作者:余鹰.E-mail:yuyingjx@163.com
更新日期/Last Update: 1900-01-01