[1]HUANG Qin,QIAN Wenbin,WANG Yinglong,et al.Multi-label feature selection algorithm for cost-sensitive data[J].CAAI Transactions on Intelligent Systems,2019,14(5):929-938.[doi:10.11992/tis.201807027]
Copy

Multi-label feature selection algorithm for cost-sensitive data

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 929-938 Column: 学术论文—人工智能基础 Public date: 2019-09-05
Title:
Multi-label feature selection algorithm for cost-sensitive data
Author(s):
HUANG Qin1; 2;  QIAN Wenbin1; 2;  WANG Yinglong1;  WU Binglong2
1. School of Computer and Information Engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2. School of Software, Jiangxi Agricultural University, Nanchang 330045, China
Keywords:
feature selection; attribute reduction; cost-sensitive; rough sets; granular computing; multi-label learning; information entropy; normal distribution
CLC:
TP391
DOI:
10.11992/tis.201807027
Abstract:
In multi-label learning, feature selection is an effective means to improve multi-label learning classification performance. Aiming at the problem that the existing multi-label feature selection methods have high computation complexity and do not consider the cost of data acquisition in real-world applications, this paper proposes a multi-label feature selection algorithm for cost-sensitive data. The algorithm first analyzes the relevance between the feature and label based on information entropy, and redefines a criterion for feature significance by employing feature test cost; it then gives a reasonable threshold selection method on the basis of the standard deviation of feature significance and feature cost that obey normal distribution. At the same time, the algorithm derives the feature subsets with low total cost by removing redundant and irrelevant features according to a threshold. Finally, the effectiveness and feasibility of the proposed algorithm are verified by the comparison and analysis of the experimental results on a multi-labeled dataset.
References:
[1] ZHANG Minling, ZHOU Zhihua. A review on multi-label learning algorithms[J]. IEEE transactions on knowledge and data engineering, 2014, 26(8):1819-1837.
[2] TSOUMAKAS G, KATAKIS I, VLAHAVAS I. Random -Labelsets for multilabel classification[J]. IEEE transactions on knowledge and data engineering, 2011, 23(7):1079-1089.
[3] 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 ZHENG Wei, WANG Chaokun, LIU Zhang, et al. A multi-label classification algorithm based on random walk model[J]. Chinese journal of computers, 2010, 33(8):1418-1426
[4] 李宇峰, 黄圣君, 周志华. 一种基于正则化的半监督多标记学习方法[J]. 计算机研究与发展, 2012, 49(6):1272-1278 LI Yufeng, HUANG Shengjun, ZHOU Zhihua. Regularized semi-supervised multi-label learning[J]. Journal of computer research and development, 2012, 49(6):1272-1278
[5] PAWLAK Z. Rough sets[J]. International journal of computer and information sciences, 1982, 11(5):341-356.
[6] PAWLAK Z, SO-Winski R. Rough set approach to multi-attribute decision analysis[J]. European journal of operational research, 1994, 72(3):443-459.
[7] 刘清. Rough集及Rough推理[M]. 北京:科学出版社, 2001.
[8] SUN Liang, JI Shuiwang, YE Jieping. Multi-label dimensionality reduction[M]. Florida:CRC Press, 2013:20–22.
[9] ZHANG Yin, ZHOU Zhihua. Multi-label dimensionality reduction via dependence maximization[C]//Proceedings of the 23rd National Conference on Artificial Intelligence. Chicago, Illinois, 2008:1503?1505.
[10] YU Kai, YU Shipeng, TRESP V. Multi-label informed latent semantic indexing[C]//Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. Salvador, Brazil, 2005:258?265.
[11] 段洁, 胡清华, 张灵均, 等. 基于邻域粗糙集的多标记分类特征选择算法[J]. 计算机研究与发展, 2015, 52(1):56-65 DUAN Jie, HU Qinghua, ZHANG Lingjun, et al. Feature selection for multi-label classification based on neighborhood rough sets[J]. Journal of computer research and development, 2015, 52(1):56-65
[12] 王晨曦, 林耀进, 唐莉, 等. 基于信息粒化的多标记特征选择算法[J]. 模式识别与人工智能, 2018, 31(2):123-131 WANG Chenxi, LIN Yaojin, TANG Li, et al. Multi-label feature selection based on information granulation[J]. Pattern recognition and artificial intelligence, 2018, 31(2):123-131
[13] LIN Yaojin, HU Qinghua, LIU Jinghua, et al. Multi-label feature selection based on neighborhood mutual information[J]. Applied soft computing, 2016, 38:244-256.
[14] 刘景华, 林梦雷, 王晨曦, 等. 基于局部子空间的多标记特征选择算法[J]. 模式识别与人工智能, 2016, 29(3):240-251 LIU Jinghua, LIN Menglei, WANG Chenxi, et al. Multi-label feature selection algorithm based on local subspace[J]. Pattern recognition and artificial intelligence, 2016, 29(3):240-251
[15] LEE J, LIM H, KIM D W. Approximating mutual information for multi-label feature selection[J]. Electronics letters, 2012, 48(15):929-930.
[16] 张振海, 李士宁, 李志刚, 等. 一类基于信息熵的多标签特征选择算法[J]. 计算机研究与发展, 2013, 50(6):1177-1184 ZHANG Zhenhai, LI Shining, LI Zhigang, et al. Multi-label feature selection algorithm based on information entropy[J]. Journal of computer research and development, 2013, 50(6):1177-1184
[17] YANG Qiang, WU Xindong. 10 challenging problems in data mining research[J]. International journal of information technology & decision making, 2006, 5(4):597-604.
[18] 徐章艳, 刘作鹏, 杨炳儒, 等. 一个复杂度为max(O(|C||U|),O(|C|2|U/C|))的快速属性约简算法[J]. 计算机学报, 2006, 29(3):391-399 XU Zhangyan, LIU Zuopeng, YANG Bingru, et al. A quick attribute reduction algorithm with complexity of max(O(|C||U|),O(|C|2|U/C|))[J]. Chinese journal of computers, 2006, 29(3):391-399
[19] WU Binglong, QIAN Wenbin, HUANG Qin, et al. Cost-Sensitive multi-label feature selection algorithm based on positive approximation[C]//Fuzzy Systems and Data Mining IV-Proceedings of FSDM 2018. Bangkok, Thailand, 2018:381?386.
[20] QIAN Yuhua, LIANG Jiye, PEDRYCZ W, et al. Positive approximation:an accelerator for attribute reduction in rough set theory[J]. Artificial intelligence, 2010, 174(9/10):597-618.
[21] WEI Wei, WU Xiaoying, LIANG Jiye, et al. Discernibility matrix based incremental attribute reduction for dynamic data[J]. Knowledge-based systems, 2018, 140:142-157.
[22] ELISSEEFF A, WESTON J. A kernel method for multi-labelled classification[C]//Proceedings of the 14th International Conference on Neural Information Processing Systems:Natural and Synthetic. Vancouver, Canada, 2001:681-687.
[23] TROHIDIS K, TSOUMAKAS G, KALLIRIS G, et al. Multi-label classification of music into emotions[C]//Proceedings of the 9th International Society for Music Information Retrieval Conference. Philadelphia, PA, 2008:325?330.
[24] BRIGGS F, HUANG Yonghong, RAICH R, et al. The 9th annual MLSP competition:new methods for acoustic classification of multiple simultaneous bird species in a noisy environment[C]//Proceedings of 2013 IEEE International Workshop on Machine Learning for Signal Processing. Southampton, UK, 2013:22?25.
Similar References:

Memo

-

Last Update: 1900-01-01

Copyright © CAAI Transactions on Intelligent Systems