Heterogeneous Ensemble Learning Algorithm Based on Label Distribution Learning

被引：0
作者：
Wang Y. [1 ,2 ]
Tian W. [1 ]
Cheng Y. [1 ,2 ]
机构：
[1] School of Computer and Information, Anqing Normal University, Anqing
[2] University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing
来源：
Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence | 2019年 / 32卷 / 10期
关键词：
Heterogeneous Ensemble Learning; Label Distribution Learning; Regression Fitting; Stacking;
D O I：
10.16451/j.cnki.issn1003-6059.201910009
中图分类号：
学科分类号：
摘要：
To improve prediction accuracy, a stacking integration framework in machine learning is employed to learn label distribution through multiple classifiers, and a heterogeneous ensemble learning algorithm based on label distribution learning(HELA-LDL) is proposed. A two-layer model framework is constructed, and the sample data are combined through the first layer structure to integrate the learning results of each classifier. Finally, the fusion results are input to the second layer classifier as the original feature, and the labels are predicted to be a label distribution with confidence. Comparative experiments on specialized datasets show that HELA-LDL is superior to other algorithms in various scenes. The stability analysis further illustrates the effectiveness of HELA-LDL. © 2019, Science Press. All right reserved.
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页码：945 / 954
页数：9
相关论文
共 17 条
  • [1] Zhang M.L., Zhou Z.H., A Review on Multi-label Learning Algorithms, IEEE Transactions on Knowledge and Data Engineering, 26, 8, pp. 1819-1837, (2014)
  • [2] Geng X., Xu N., Shao R.F., Label Enhancement for Label Distribution Learning, Journal of Computer Research and Development, 54, 6, pp. 1171-1184, (2017)
  • [3] Geng X., Label Distribution Learning, IEEE Transactions on Knowledge and Data Engineering, 28, 7, pp. 1734-1748, (2016)
  • [4] Geng X., Yin C., Zhou Z.H., Facial Age Estimation by Learning from Label Distributions, IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 10, pp. 2401-2412, (2013)
  • [5] He Z.Z., Li X., Zhang Z.F., Et al., Data-Dependent Label Distribution Learning for Age Estimation, IEEE Transactions on Image Processing, 26, 8, pp. 3846-3858, (2017)
  • [6] Zhang Z.X., Wang M., Geng X., Crowd Counting in Public Vi-deo Surveillance by Label Distribution Learning, Neurocomputing, 166, pp. 151-163, (2015)
  • [7] Gao B.B., Xing C., Xie C.W., Et al., Deep Label Distribution Learning with Label Ambiguity, IEEE Transactions on Image Processing, 26, 6, pp. 2825-2838, (2017)
  • [8] Zhou Y., Xue H., Geng X., Emotion Distribution Recognition from Facial Expressions, Proc of the 23rd ACM Conference on Multimedia, pp. 1247-1250, (2015)
  • [9] Zhao Q., Geng X., Selection of Target Function in Label Distribution Learning, Journal of Frontiers of Computer Science and Technology, 11, 5, pp. 708-719, (2017)
  • [10] Cui Y., Xu K., Lu Z.J., Et al., Combination Strategy of Active Learning for Hyperspectral Images Classification, Journal on Communications, 39, 4, pp. 91-99, (2018)
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