Multi-label learning with label-specific feature reduction

doi:10.11772/j.issn.1001-9081.2015.11.3218

Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (11): 3218-3221.DOI: 10.11772/j.issn.1001-9081.2015.11.3218

• CRSSC 2015 Paper • Previous Articles Next Articles

Multi-label learning with label-specific feature reduction

XU Suping, YANG Xibei, QI Yunsong

School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang Jiangsu 212003, China

Received:2015-06-20 Revised:2015-07-13 Published:2015-11-13

基于类属属性约简的多标记学习

徐苏平, 杨习贝, 祁云嵩

江苏科技大学计算机科学与工程学院, 江苏镇江 212003

通讯作者: 杨习贝(1980-),男,江苏镇江人,副教授,博士,主要研究方向:粗糙集理论、粒计算、机器学习.
作者简介:徐苏平(1991-),男,江苏扬州人,硕士研究生,主要研究方向:机器学习、粗糙集理论; 祁云嵩(1967-),男,江苏如皋人,教授,博士,主要研究方向:机器学习、智能信息处理.
基金资助:
国家自然科学基金资助项目(61471182,61100116,61305058);江苏省自然科学基金资助项目(BK2012700,BK20130471);中国博士后科学基金资助项目(2014M550293).

Abstract

Abstract: In multi-label learning, since different labels may have their own characteristics, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may increase the dimension of feature vector, which brings some redundant information in feature space. To solve this problem, a multi-label learning approach named FRS-LIFT was presented, which can implement label-specific feature reduction by fuzzy rough set. FRS-LIFT contains four steps: construction of label-specific features, reduction of feature dimensionality, training of classification models and prediction of unknown samples. The experimental results on 5 multi-label datasets show that, compared with LIFT, the proposed method can not only reduce the dimension of label-specific features, but also achieve satisfactory performances in 5 evaluation metrics.

Key words: feature reduction, fuzzy rough set, label-specific feature, multi-label learning

摘要： 在多标记学习中,由于不同的标记可能会带有自身的一些特性,所以目前已经出现了基于标记类属属性的多标记学习算法LIFT.然而,类属属性的构建可能会增加属性向量的维度,致使属性空间存在冗余信息.为此,借助模糊粗糙集提出了一种能够进行类属属性约简的多标记学习算法FRS-LIFT,其包含4个步骤:类属属性构建、属性维度约简、分类模型训练和未知样本预测.在5个多标记数据集上的实验结果表明,该算法与LIFT算法相比,不仅能够降低类属属性维数,而且在5种多标记评价指标上均具有较好的实验效果.

关键词: 属性约简, 模糊粗糙集, 类属属性, 多标记学习

CLC Number:

TP18

XU Suping, YANG Xibei, QI Yunsong. Multi-label learning with label-specific feature reduction[J]. Journal of Computer Applications, 2015, 35(11): 3218-3221.

徐苏平, 杨习贝, 祁云嵩. 基于类属属性约简的多标记学习[J]. 计算机应用, 2015, 35(11): 3218-3221.

References

[1] BOUTELL M R, LUO J, SHEN X, et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004, 37(9): 1757-1771.
[2] CHANG E, GOH K, SYCHAY G, et al. CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2003, 13(1): 26-38.
[3] CHOU K C, SHEN H. Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites[J]. Journal of Proteome Research, 2007, 6(5): 1728-1734.
[4] GLORY E, MURPHY R F. Automated subcellular location determination and high-throughput microscopy[J]. Developmental Cell, 2007, 12(1): 7-16.
[5] ZHANG M, ZHOU Z. A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(8): 1819-1837.
[6] TSOUMAKAS G, KATAKIS I. Multi-label classification: an overview[J]. International Journal of Data Warehousing and Mining, 2007, 3(3): 12-16.
[7] SCHAPIRE R E, SINGER Y. BoosTexter: a boosting-based system for text categorization[J]. Machine Learning, 2000, 39(2): 135-168.
[8] TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Random k-labelsets for multi-label classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(7): 1079-1089.
[9] ZHANG M, ZHOU Z. ML-KNN: a lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40(7): 2038-2048.
[10] ZHANG M, PENA J M, ROBLES V. Feature selection for multi-label naive Bayes classification[J]. Information Sciences, 2009, 179(19): 3218-3229.
[11] TSOUMAKAS G, VLAHAVAS I. Random k-Labelsets: an ensemble method for multi-label classification[C]// Proceedings of the 18th European Conference on Machine Learning. Heidelberg: Springer-Verlag, 2007: 406-417.
[12] DIMOU A, TSOUMAKAS G, MEZARIS V, et al. An empirical study of multi-label learning methods for video annotation[C]// Proceedings of the 7th International Workshop on Content-Based Multimedia Indexing. Piscataway: IEEE, 2009: 19-24.
[13] ZHANG M. LIFT: multi-label learning with label-specific features[C]// Proceedings of the 22nd International Joint Conference on Artificial Intelligence. Piscataway: IEEE, 2011: 1609-1614.
[14] DUBOIS D, PRADE H. Rough fuzzy sets and fuzzy rough sets[J]. International Journal of General System, 1990, 17(2): 191-209.
[15] JAIN A K, MURTY M N, FLYNN P J. Data clustering: a review[J]. ACM Computing Surveys, 1999, 31(3): 264-323.
[16] YANG X, QI Y, SONG X, et al. Test cost sensitive multigranulation rough set: model and minimal cost selection[J]. Information Sciences, 2013, 250(11): 184-199.
[17] YANG X, QI Y, YU H, et al. Updating multigranulation rough approximations with increasing of granular structures[J]. Knowledge-Based Systems, 2014, 64: 59-69.
[18] GHAMRAWI N, MCCALLUM A. Collective multi-label classification[C]// Proceedings of the 14th ACM International Conference on Information and Knowledge Management. New York: ACM, 2005: 195-200.
[19] GODBOLE S, SARAWAGI S. Discriminative methods for multi-labeled classification[C]// Proceedings of the 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining. Berlin: Springer-Verlag, 2004: 22-30.

Multi-label learning with label-specific feature reduction

基于类属属性约简的多标记学习

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	WANG Xiaorong, ZHANG Yuzhao, ZHANG Zhenjiang. Selection of express freight transportation schemes based on rough set over two universes [J]. Journal of Computer Applications, 2021, 41(5): 1500-1505.
[2]	ZHANG Zhihao, LIN Yaojin, LU Shun, GUO Chen, WANG Chenxi. Multi-label feature selection based on label-specific feature with missing labels [J]. Journal of Computer Applications, 2021, 41(10): 2849-2857.
[3]	LIN Tengtao, ZHA Siming, CHEN Lei, LONG Xianzhong. Graph trend filtering guided noise tolerant multi-label learning model [J]. Journal of Computer Applications, 2021, 41(1): 8-14.
[4]	CAO Jianfang, ZHAO Aidi, ZHANG Zibang. Application of convolutional neural network with threshold optimization in image annotation [J]. Journal of Computer Applications, 2020, 40(6): 1587-1592.
[5]	ZHANG Wu, CHEN Hongmei. Hyperspectral band selection based on multi-kernelized fuzzy rough set and grasshopper optimization algorithm [J]. Journal of Computer Applications, 2020, 40(5): 1425-1430.
[6]	CHENG Yusheng, SONG Fan, WANG Yibin, QIAN Kun. Multi-label feature selection algorithm based on conditional mutual information of expert feature [J]. Journal of Computer Applications, 2020, 40(2): 503-509.
[7]	ZHANG Wu, CHEN Hongmei. Hyperspectral band selection algorithm based on kernelized fuzzy rough set [J]. Journal of Computer Applications, 2020, 40(1): 258-263.
[8]	LAI Jie, WANG Xiaodan, LI Rui, ZHAO Zhenchong. Denoising autoencoder based extreme learning machine [J]. Journal of Computer Applications, 2019, 39(6): 1619-1625.
[9]	CHENG Yusheng, QIAN Kun, WANG Yibing, ZHAO Dawei. Multi-label lazy learning approach based on firefly method [J]. Journal of Computer Applications, 2019, 39(5): 1305-1311.
[10]	GUO Xudong, LI Xiaomin, JING Ruxue, GAO Yuzhuo. Intrusion detection based on improved sparse denoising autoencoder [J]. Journal of Computer Applications, 2019, 39(3): 769-773.
[11]	XU Hongfeng, SUN Zhenqiang. Fast feature selection method based on mutual information in multi-label learning [J]. Journal of Computer Applications, 2019, 39(10): 2815-2821.
[12]	WAN Zhichao, SONG Jie, SHENG Yongliang. Variable intuitionistic fuzzy multi-granulation rough set model and its approximate distribution reduction algorithms [J]. Journal of Computer Applications, 2018, 38(2): 390-398.
[13]	GAO Yaodong, HOU Lingyan, YANG Dali. Automatic image annotation method using multi-label learning convolutional neural network [J]. Journal of Computer Applications, 2017, 37(1): 228-232.
[14]	CHENG Bo, ZHU Bingli, XIONG Jiang. Multimodal multi-label transfer learning for early diagnosis of Alzheimer's disease [J]. Journal of Computer Applications, 2016, 36(8): 2282-2286.
[15]	WEN Wen, WU Biao, CAI Ruichu, HAO Zhifeng, WANG Lijuan. Emotion classification for news readers based on multi-category semantic word clusters [J]. Journal of Computer Applications, 2016, 36(8): 2076-2081.