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 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]





A multi-label classification algorithm based on an improved convolutional neural network
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
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
multi-label learningconvolutional neural networktransfer learningfully-connected layerfeature expressionmulti-label classificationdeep learningloss function
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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更新日期/Last Update: 1900-01-01