Multi-Label Evolutionary Hypernetwork Based on Label Correlations

被引：0
|
作者：
Wang J. [1 ]
Liu B. [1 ]
Sun K.-W. [1 ]
Chen Q.-S. [1 ]
Deng X. [1 ]
机构：
[1] Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing
来源：
| 2018年 / Chinese Institute of Electronics卷 / 46期
关键词：
Evolutionary hypernetwork; Label correlation; Machine learning; Multi-label learning;
D O I：
10.3969/j.issn.0372-2112.2018.04.032
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to solve the problem that how to explore and exploit the high-order label correlations effectively in multi-label learning, a Multi-Label evolutionary HyperNetwork based on label Correlations (MLHNC) is proposed in this paper. In MLHNC, the predicting results obtained from any multi-label learning method are utilized as input of the model, the high-order correlations among labels are represented and explored by hyperedges, and the final prediction is made by integrating the label correlation and feature information. The experimental results on six multi-label datasets compared with three state-of-the-art multi-label learning methods show that the MLHNC not only improves the performance of various state-of-the-art multi-label learning methods, but also provides readable learning results. © 2018, Chinese Institute of Electronics. All right reserved.
引用
收藏
页码：1012 / 1018
页数：6
相关论文
共 22 条
  • [1] Luo H.-L., Guo M.-J., Kong F.-S., An image classification method based on multiple level spatial visual dictionary ensemble, Acta Electronica Sinica, 43, 4, pp. 684-693, (2015)
  • [2] Tsoumakas G., Katakis I., Vlahavas I., Mining Multi-Label Data. Data Mining and Knowledge Discovery Handbook, pp. 667-685, (2010)
  • [3] 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)
  • [4] Li S.-N., Li N., Li Z.-H., Multi-label data mining: a survey, Computer Science, 4, 4, pp. 14-21, (2013)
  • [5] Boutell M.R., Luo J.B., Shen X.P., Et al., Learning multi-label scene classification, Pattern Recognition, 37, 9, pp. 1757-1771, (2004)
  • [6] Yu Y., Pedrycz W., Miao D., Neighborhood rough sets based multi-label classification for automatic image annotation, International Journal of Approximate Reasoning, 54, 9, pp. 1373-1387, (2013)
  • [7] Liu J., Du J.-P., Latent topic fusion-based cross-media image semantic annotation, Acta Electronica Sinica, 42, 5, pp. 987-991, (2014)
  • [8] Schapire R.E., Singer Y., BoosTexter: a boosting-based system for text categorization, Machine Learning, 39, 2-3, pp. 135-168, (2000)
  • [9] Ianculef R., Flaounas I., Cristianini N., Efficient classification of multi-labeled text streams by clashing, Expert Systems with Applications, 41, 11, pp. 5431-5450, (2014)
  • [10] Jiang Y.-Y., Li P., Wang Q., Labeled LDA model based on shared background topics, Acta Electronica Sinica, 41, 9, pp. 1794-1799, (2013)
123