基于标签关联的多标签演化超网络

doi:10.3969/j.issn.0372-2112.2018.04.032

电子学报 ›› 2018, Vol. 46 ›› Issue (4): 1012-1018.DOI: 10.3969/j.issn.0372-2112.2018.04.032

基于标签关联的多标签演化超网络

王进, 刘彬, 孙开伟, 陈乔松, 邓欣

重庆邮电大学计算智能重庆市重点实验室, 重庆 400065

收稿日期:2016-05-12 修回日期:2017-07-14 出版日期:2018-04-25
- 作者简介:
- 王进男,1979年1月出生于重庆,工学博士,2008年在韩国仁荷大学获得工学博士学位,现为重庆邮电大学教授.主要研究方向为数据挖掘、机器学习、智能交通.E-mail:wangjin@cqupt.edu.cn;刘彬男,1989年11月出生于河北保定,2016年在重庆邮电大学获得工学硕士学位,现为重庆邮电大学助教.主要研究方向为数据挖掘
- 基金资助:
- 重庆市基础与前沿研究计划 (No.cstc2014jcyjA40001，No.cstc2014jcyjA40022）; 重庆教委科学技术研究项目 (自然科学类） (No.KJ1400436）

Multi-Label Evolutionary Hypernetwork Based on Label Correlations

WANG Jin, LIU Bin, SUN Kai-wei, CHEN Qiao-song, DENG Xin

Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received:2016-05-12 Revised:2017-07-14 Online:2018-04-25 Published:2018-04-25

摘要/Abstract

摘要： 针对多标签学习中如何有效挖掘利用高阶标签关联的问题，提出了一种基于标签关联的多标签演化超网络模型.该模型通过输入任意多标签学习方法的预测结果，利用超边表征挖掘高阶标签关联，并综合标签关联和特征信息作为最终的预测结果.与3种传统多标签学习方法在6个多标签数据集上的对比实验表明，本文提出模型不仅能够有效提升多个传统多标签学习方法的性能，而且能够提供具有良好可读性的学习结果.

关键词: 机器学习, 多标签学习, 演化超网络, 标签关联

Abstract:

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.

Key words: machine learning, multi-label learning, evolutionary hypernetwork, label correlation

中图分类号:

TP39

王进, 刘彬, 孙开伟, 陈乔松, 邓欣. 基于标签关联的多标签演化超网络[J]. 电子学报, 2018, 46(4): 1012-1018.

WANG Jin, LIU Bin, SUN Kai-wei, CHEN Qiao-song, DENG Xin. Multi-Label Evolutionary Hypernetwork Based on Label Correlations[J]. Acta Electronica Sinica, 2018, 46(4): 1012-1018.

参考文献

[1] 罗会兰,郭敏杰,孔繁胜.一种基于多级空间视觉词典集体的图像分类方法[J].电子学报,2015,43(4):684-693. LUO Hui-lan,GUO Min-jie,KONG Fan-sheng.An image classification method based on multiple level spatial visual dictionary ensemble[J].Acta Electronica Sinica,2015,43(4):684-693.(in Chinese)
[2] TSOUMAKAS G,KATAKIS I,VLAHAVAS I.Mining Multi-Label Data[A].Data Mining and Knowledge Discovery Handbook[M].Springer,2010.667-85.
[3] ZHANG M L,ZHOU Z H.A review on multi-label learning algorithms[J].IEEE Transactions on Knowledge and Data Engineering,2014,26(8):1819-1837.
[4] 李思男,李宁,李战怀.多标签数据挖掘技术:研究综述[J].计算机科学,2013,4(4):14-21. LI Si-nan,LI Ning,LI Zhan-huai.Multi-label data mining:a survey[J].Computer Science,2013,4(4):14-21.(in Chinese)
[5] BOUTELL M R,LUO J B,SHEN X P,et al.Learning multi-label scene classification[J].Pattern Recognition,2004,37(9):1757-1771.
[6] YU Y,PEDRYCZ W,MIAO D.Neighborhood rough sets based multi-label classification for automatic image annotation[J].International Journal of Approximate Reasoning,2013,54(9):1373-1387.
[7] 刘杰,杜军平.基于潜在主题融合的跨媒体图像语义标注[J].电子学报,2014,42(5):987-991. LIU Jie,DU Jun-ping.Latent topic fusion-based cross-media image semantic annotation[J].Acta Electronica Sinica,2014,42(5):987-991.(in Chinese)
[8] SCHAPIRE R E,SINGER Y.BoosTexter:a boosting-based system for text categorization[J].Machine Learning,2000,39(2-3):135-68.
[9] ÎANCULEF R,FLAOUNAS I,CRISTIANINI N.Efficient classification of multi-labeled text streams by clashing[J].Expert Systems with Applications,2014,41(11):5431-5450.
[10] 江雨燕,李平,王清.基于共享背景主题的Labeled LDA模型[J].电子学报,2013,41(9):1794-1799. JIANG Yu-yan,LI Ping,WANG Qing.Labeled LDA model based on shared background topics[J].Acta Electronica Sinica,2013,41(9):1794-1799.(in Chinese)
[11] ZHANG M L,ZHOU Z H.Multilabel neural networks with applications to functional genomics and text categorization[J].IEEE Transactions on Knowledge and Data Engineering,2006,18(10):1338-1351.
[12] YU G X,RANGWALA H,DOMENICONI C,et al.Protein function prediction with incomplete annotations[J].IEEE Transactions on Computational Biology and Bioinformatics,2014,11(3):579-591.
[13] LO H-Y,WANG J-C,WANG H-M,et al.Cost-sensitive multi-label learning for audio tag annotation and retrieval[J].IEEE Transactions on Multimedia,2011,13(3):518-529.
[14] ZHANG B T.Hypernetworks:a molecular evolutionary architecture for cognitive learning and memory[J].IEEE Computational Intelligence Magazine,2008,3(3):49-63.
[15] ZHANG M L,ZHOU Z H.ML-KNN:A lazy learning approach to multi-label learning[J].Pattern Recognition,2007,40(7):2038-2048.
[16] RNKRANZ J F,LLERMEIER E H,MENC A E L,et al.Multilabel classification via calibrated label ranking[J].Machine Learning,2008,73(2):133-153.
[17] TSOUMAKAS G,KATAKIS I,VLAHAVAS L.Random k-labelsets for multilabel classification[J].IEEE Transactions on Knowledge and Data Engineering,2011,23(7):1079-1089.
[18] READ J,PFAHRINGER B,HOLMES G,et al.Classifier chains for multi-label classification[J].Machine Learning,2011,85(3):333-359.
[19] MONTA ES E,SENGE R,BARRANQUERO J,et al.Dependent binary relevance models for multi-label classification[J].Pattern Recognition,2014,47(3):1494-1508.
[20] SUN K W,LEE C H,XIE X F.MLHN:a hypernetwork model for multi-label classification[J].International Journal of Pattern Recognition & Artificial Intelligence,2015,26(6):1550020.
[21] KIM S J,HA J W,ZHANG B T.Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes[J].Journal of Biomedical Informatics,2014,49(6):101-111.
[22] TSOUMAKAS G,SPYROMITROS-XIOUFIS E,VILCEK J,et al.Mulan:A java library for multi-label learning[J].The Journal of Machine Learning Research,2011,12(7):2411-2414.

基于标签关联的多标签演化超网络

Multi-Label Evolutionary Hypernetwork Based on Label Correlations

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	李雪莹, 王田路, 梁鹏, 王翀. 基于系统模型的用户评论中非功能需求的自动分类[J]. 电子学报, 2022, 50(9): 2079-2089.
[2]	黄璐, 蔚保国, 李宏生, 李隽, 贾浩男, 程建强, 李雅宁. GNSS拒止环境下的伪卫星指纹定位方法[J]. 电子学报, 2022, 50(4): 811-822.
[3]	崔亚奇, 何友, 唐田田, 熊伟. 一种深度学习航迹关联方法[J]. 电子学报, 2022, 50(3): 759-763.
[4]	蒋华伟, 郭陶, 杨震. 车辆路径问题研究进展[J]. 电子学报, 2022, 50(2): 480-492.
[5]	郑锐, 汪秋云, 林卓庞, 靖蓉琦, 姜政伟, 傅建明, 汪姝玮. 一种基于威胁情报层次特征集成的挖矿恶意软件检测方法[J]. 电子学报, 2022, 50(11): 2707-2715.
[6]	张梦, 郑建宏, 刘香燕, 何云. 基于集成学习的全双工中继系统安全中继选择方案研究[J]. 电子学报, 2021, 49(9): 1852-1856.
[7]	郑海颖, 王峰, 姜维, 王志强, 姚西文. 神经网络训练策略对高分辨率遥感图像场景分类性能影响的评估[J]. 电子学报, 2021, 49(8): 1599-1614.
[8]	周东明, 张灿龙, 李志欣, 王智文. 基于多层级视觉融合的图像描述模型[J]. 电子学报, 2021, 49(7): 1286-1290.
[9]	侯秀聪, 赖晓平, 曹九稳. 正则化超限学习机的最大分划广义交替方向乘子法[J]. 电子学报, 2021, 49(4): 625-630.
[10]	陈长青, 郭春, 崔允贺, 申国伟, 蒋朝惠. 基于API短序列的勒索软件早期检测方法[J]. 电子学报, 2021, 49(3): 586-595.
[11]	刘润滋, 吴伟华, 张文柱, 周笛, 张琰. 基于图学习的密集空间网络传输资源调度方法[J]. 电子学报, 2021, 49(11): 2133-2137.
[12]	仇祝令, 查宇飞, 吴敏, 王青. 基于注意力学习的正则化相关滤波跟踪算法[J]. 电子学报, 2020, 48(9): 1762-1768.
[13]	宋宇琦, 高旻, 李骏东, 荣文戈, 熊庆宇. 网络欺凌检测综述[J]. 电子学报, 2020, 48(6): 1220-1229.
[14]	蒋留兵, 周小龙, 车俐. 基于无载波超宽带雷达的小样本人体动作识别[J]. 电子学报, 2020, 48(3): 602-615.
[15]	高鹏, 陈智华. 一种基于拓扑信息的预测疾病相关的MicroRNAs方法[J]. 电子学报, 2020, 48(2): 333-340.