基于多标签随机游走的选择性集成方法用于表情识别
A Selective Ensemble Method Based on Multi-Label Random Walk for Expression Recognition
摘要: 为了提升分类器集成的性能，本文提出了一种基于多标签随机游走的选择性集成方法，该方法将分类器选择问题建模为多标签分类问题，以灵活有效选择分类器。首先在训练集样本与分类器序列间建立映射，将能正确识别样本的分类器序列视为样本标签，对于一个特定的测试样本，寻找它的近邻样本，并构建出多标签随机游走图，执行随机游走过程，根据收敛后的概率向量选择出对应的分类器序列进行集成。在Fer2013、CK+和JAFFE人脸表情数据集上进行实验，并与一些当前先进的选择性集成算法进行对比，实验结果证明了该算法的可行性和有效性。
Abstract: In order to improve the ensemble performance of classifier ensemble, a selective ensemble method based on multi-label random walk is proposed, which models the classifier selection problem as a multi-label classification problem to select classifiers flexibly and effectively. First, a mapping is established between the training set samples and the classifier sequence, and the classifier sequence that can correctly identify the sample is regarded as the sample label. For a specific test sample, its neighbor samples are found, and a multi-label random walk graph is constructed. A random walk process is performed, and the corresponding classifier sequence is selected for ensemble according to the converged probability vector. Experiments are carried out on Fer2013, CK+ and JAFFE facial expression datasets, and compared with some current advanced selective ensemble algorithms. The experimental results demonstrate the feasibility and effectiveness of the algorithm.
文章引用：黄仕松, 李丹杨, 陈星, 唐玉梅, 吴义青. 基于多标签随机游走的选择性集成方法用于表情识别[J]. 软件工程与应用, 2022, 11(6): 1344-1356. https://doi.org/10.12677/SEA.2022.116138

参考文献

[1] 周伟航, 肖正清, 钱育蓉, 等. 微表情自动分析方法研究综述[J]. 计算机应用研究, 2022, 39(7): 1921-1932.
[2] Ge, H.L., Zhu, Z.Y., Dai, Y.W., et al. (2022) Facial Expression Recognition Based on Deep Learning. Computer Methods and Programs in Biomedicine, 215, Article ID: 106621. [Google Scholar] [CrossRef] [PubMed]
[3] Li, D.Y., Wen, G.H., Hou, Z., et al. (2019) RTCRelief-F: An Effective Clustering and Ordering-Based Ensemble Pruning Algorithm for Facial Expression Recognition. Knowledge and Information Systems, 59, 219-250. [Google Scholar] [CrossRef] 
[4] Liang, Z.F., Wang, H., Yang, K.X., et al. (2022) Adaptive Fusion Based Method for Imbalanced Data Classification. Frontiers in Neurorobotics, 16, Article ID: 827913. [Google Scholar] [CrossRef] [PubMed]
[5] Cruz, R., Sabourin, R. and Cavalcanti, G. (2018) Prototype Selection for Dynamic Classifier and Ensemble Selection. Neural Computing and Applications, 29, 447-457. [Google Scholar] [CrossRef] 
[6] Li, D.Y. and Wen, G.H. (2018) MRMR-Based Ensemble Pruning for Facial Expression Recognition. Multimedia Tools and Applications, 77, 15251-15272. [Google Scholar] [CrossRef] 
[7] Zhang, C., Wu, Y. and Zhu, M. (2019) Pruning Variable Selection Ensembles. Statistical Analysis and Data Mining, 12, 168-184. [Google Scholar] [CrossRef] 
[8] Elmi, J. and Eftekhari, M. (2021) Multi-Layer Selector (MLS): Dynamic Selection Based on Filtering Some Competence Measures. Applied Soft Computing, 104, Article ID: 107257. [Google Scholar] [CrossRef] 
[9] 饶川, 苟先太, 金炜东. 基于选择性集成学习的高速列车故障识别研究[J]. 计算机应用研究, 2018, 35(5): 1365-1367.
[10] Jan, Z. and Verma, B. (2019) A Novel Diversity Measure and Classifier Selection Approach for Generating Ensemble Classifiers. IEEE Access, 7, 156360-156373. [Google Scholar] [CrossRef] 
[11] Wang, Z.W., Wang, S.K., Wan, B.T., et al. (2020) A Novel Multi-Label Classification Algorithm Based on K-Nearest Neighbor and Random Walk. International Journal of Distributed Sensor Networks, 16, 1-17. [Google Scholar] [CrossRef] 
[12] 郑伟, 王朝坤, 刘璋, 等. 一种基于随机游走模型的多标签分类算法[J]. 计算机学报, 2010, 33(8): 1418-1426.
[13] Goodfellow, I.J., Erhan, D., Carrier, P.L., et al. (2015) Challenges in Representation Learning: A Report on Three Machine Learning Contests. Neural Networks, 64, 59-63. [Google Scholar] [CrossRef] [PubMed]
[14] Lucey, P., Cohn, J.F., Kanade, T., et al. (2010) The Extended Cohn-Kanade Dataset (CK+): A Complete Dataset for Action Unit and Emotion-Specified Expression. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 13-18 June 2010, 94-101. [Google Scholar] [CrossRef] 
[15] Lyons, M.J., Akamatsu, S., Kamachi, M.G., et al. (2002) Coding Facial Expressions with Gabor Wavelets. 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Nara, 14-16 April 1998, 200-205.
[16] Li, D.Y., Wen, G.H., Li, X., et al. (2019) Graph-Based Dynamic Ensemble Pruning for Facial Expression Recognition. Applied Intelligence, 49, 3188-3206. [Google Scholar] [CrossRef] 
[17] Krizhevsky, A., Sutskever, I. and Hinton, G.E. (2017) ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM, 60, 84-90. [Google Scholar] [CrossRef] 
[18] Ma, H. and Celik, T. (2019) FER-Net: Facial Expression Recognition Using Densely Connected Convolutional Network. Electronics Letters, 55, 184-186. [Google Scholar] [CrossRef] 
[19] Dai, Q. and Han, X.M. (2016) An Efficient Ordering-Based Ensemble Pruning Algorithm via Dynamic Programming. Applied Intelligence, 44, 816-830. [Google Scholar] [CrossRef] 
[20] Li, N., Yu, Y. and Zhou, Z.H. (2012) Diversity Regularized Ensemble Pruning, Machine Learning and Knowledge Discovery in Databases. Proceedings of the European Conference (ECML PKDD 2012), Bristol, 24-28 September 2012, 330-345. [Google Scholar] [CrossRef] 
[21] Kuncheva, L.I. (2013) A Bound on Kappa-Error Diagrams for Analysis of Classifier Ensemble. IEEE Transactions on Knowledge and Data Engineering, 25, 494-501. [Google Scholar] [CrossRef] 
[22] Kuncheva, L.I. and Whitaker, C.J. (2003) Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy. Machine Learning, 51, 181-207. [Google Scholar] [CrossRef] 
[23] Markatopoulou, F., Tsoumakas, G. and Vlahavas, I. (2014) Dynamic Ensemble Pruning Based on Multi-Label Classification. Neurocomputing, 150, 501-512. [Google Scholar] [CrossRef] 
[24] Albert, H.R., Ko, R.S., et al. (2008) From Dynamic Classifier Selection to Dynamic Ensemble Selection. Pattern Recognition, 41, 1718-1731. [Google Scholar] [CrossRef] 
[25] Hou, C., Xia, Y., Xu, Z., et al. (2016) Learning Classifier Competence Based on Graph for Dynamic Classifier Selection. 2016 12th International Conference on Natural Computation and 13th Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Changsha, 13-15 August 2016, 1164-1168. [Google Scholar] [CrossRef] 

为你推荐