[1]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(3):566-574.[doi:10.11992/tis.201804056]
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A multi-label classification algorithm based on an improved convolutional neural network

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 3 Page number: 566-574 Column: 学术论文—机器学习 Public date: 2019-05-05
Title:
A multi-label classification algorithm based on an improved convolutional neural network
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
Keywords:
multi-label learning; convolutional neural network; transfer learning; fully-connected layer; feature expression; multi-label classification; deep learning; loss function
CLC:
TP181
DOI:
10.11992/tis.201804056
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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