基于改进卷积神经网络的多标记分类算法
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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。
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.
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.
引文
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[3]JIANG J Q,MCQUAY L J.Predicting protein function by multi-label correlated semi-supervised learning[J].IEEE/ACM transactions on computational biology and bioinformatics,2012,9(4):1059-1069.
[4]OZONAT K,YOUNG D.Towards a universal marketplace over the web:statistical multi-label classification of service provider forms with simulated annealing[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.Paris,France,2009:1295-1304.
[5]GONG Y,JIA Y,LEUNG T,et al.Deep convolutional ranking for multilabel image annotation[C].2nd International Conference on Learning Representations,ICLR2014.Banff,Canada,2014:1312-1320.
[6]ZHANG Minling,ZHOU Zhihua.A review on multi-label learning algorithms[J].IEEE transactions on knowledge and data engineering,2014,26(8):1819-1837.
[7]LUACES O,DíEZ J,BARRANQUERO J,et al.Binary relevance efficacy for multilabel classification[J].Progress in artificial intelligence,2012,1(4):303-313.
[8]READ J,PFAHRINGER B,HOLMES G.Multi-label classification using ensembles of pruned sets[C]//ICDM'08.Eighth IEEE International Conference on Data Mining.Pisa,Italy,2008:995-1000.
[9]WAN Shupeng,XU Jianhua.A multi-label classification algorithm based on triple class support vector machine[C]//Proceedings of 2007 International Conference on Wavelet Analysis and Pattern Recognition.Beijing,China,2008:1447-1452.
[10]张敏灵.一种新型多标记懒惰学习算法[J].计算机研究与发展,2012,49(11):2271-2282.ZHANG Minling.An improved multi-label lazy learning approach[J].Journal of computer research and development,2012,49(11):2271-2282.
[11]ZHANG Mimling.ML-RBF:RBF neural networks for multi-label learning[J].Neural processing letters,2009,29(2):61-74.
[12]WANG Jiang,YANG Yi,MAO Junhua,et al.CNN-RNN:a unified framework for multi-label image classification[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,USA,2016:2285-2294.
[13]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems.Lake Tahoe,USA,2012:1097-1105.
[14]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations,ICLR 2015.San Diego,USA,2015:1409-1422.
[15]DAI Wenyuan,XUE Guirong,YANG Qiang,et al.Coclustering based classification for out-of-domain documents[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.San Jose,USA,2007:210-219.
[16]ZHUO H H,YANG Qiang.Action-model acquisition for planning via transfer learning[J].Artificial intelligence,2014,212:80-103.
[17]DENG Jia,DONG Wei,SOCHER R,et al.ImageNet:a large-scale hierarchical image database[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition.Miami,USA,2009:248-255.
[18]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the Inception Architecture for Computer Vision[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas,USA,2016:2818-2826.
[19]EVERINGHAM M,VAN GOOL L,WILLIAMS C K I,et al.The pascal visual object classes(VOC)challenge[J].International journal of computer vision,2010,88(2):303-338.
[20]SHELHAMER E,LONG J,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE transactions on pattern analysis and machine intelligence,2017,39(4):640-651.
[21]ZHANG Chenlin,LUO Jianhao,WEI Xiushen,et al.In defense of fully connected layers in visual representation transfer[C]//Proceedings of the 18th Pacific-Rim Conference on Multimedia on Advances in Multimedia Information Processing.Harbin,China,2017:807-817.
[22]HARZALLAH H,JURIE F,SCHMID C.Combining efficient object localization and image classification[C]//Proceedings of 2009 IEEE International Conference on Computer Vision.Kyoto,Japan,2009:237-244.
[23]PERRONNIN F,SáNCHEZ J,MENSINK T.Improving the fisher kernel for large-scale image classification[C]//Proceedings of the 11th European Conference on Computer Vision.Heraklion,Greece,2010:143-156.
[24]YANG Jingjing,LI Yuanning,TIAN Yonghong,et al.Group-sensitive multiple kernel learning for object categorization[C]//Proceedings of 2009 IEEE International Conference on Computer Vision.Kyoto,Japan,2009:436-443.
[25]OQUAB M,BOTTOU L,LAPTEV I,et al.Learning and transferring mid-level image representations using convolutional neural networks[C]//Proceedings of 2014 IEEEConference on Computer Vision and Pattern Recognition.Columbus,USA,2014:1717-1724.
[26]WEI Y,XIA W,LIN M,et al.HCP:a flexible CNNframework for multi-label image classification[J].IEEEtransactions on pattern analysis and machine intelligence,2016,38(9):1901-1907.