JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (3): 81-88.doi: 10.6040/j.issn.1671-9352.1.2019.162

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A multi-label three-way classification algorithm based on label correlation

Ying YU*,Xin-nian WU(),Le-wei WANG,Ying-long ZHANG   

  1. College of Software, East China Jiaotong University, Nanchang 330013, Jiangxi, China
  • Received:2019-05-21 Online:2020-03-20 Published:2020-03-27
  • Contact: Ying YU E-mail:yuyingjx@163.com

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Abstract:

This paper uses the probability map model to the tag relationship is encoded, and three-way three decision models are used to solve the uncertainty of the data samples. A multi-label classification algorithm based on three-way decision-correlation correlation is proposed. The algorithm will solve the two-way decision problem in multi-label classification(TML_LC). The SVM mapping is divided into accepted domain, rejected domain and uncertain domain. The probability map model is used to consider the correlation between labels to transform the uncertainty of the uncertain domain, so as to improve the accuracy of the classification model.

Key words: multi-label learning, three-way decision, label correlation, delayed decision

CLC Number: 

  • TP391

Fig.1

Multi-label image"

Fig.2

SVM classification with boundary threshold"

Fig.3

Three decisions based on single evaluation function"

Table 1

A example of label correlation matri"

l1 l2 l3 l4
l1 0 1 0 1
l2 1 0 1 0
l3 0 0 0 0
l4 0 0 1 0

Table 2

Description of the experimental data set"

数据集名称 样本个数 特征个数 标记个数 平均标记数 标记密度
yeast 2 417 103 14 4.237 0.303
scene 2 407 294 6 1.074 0.179
emotions 593 72 6 1.869 0.311

Fig.4

Average precision varies with βand αon yeast"

Fig.5

Average precision varies with αand βon scene"

Fig.6

Average Precision varies with βand α on emotions"

Table 3

Hamming loss of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.198±0.013 0.199±0.010 0.195±0.011 0.205±0.010 0.208±0.010
emotions 0.195±0.011 0.199±0.022 0.194±0.013 0.219±0.021 0.192±0.021
scene 0.101±0.006 0.104±0.006 0.084±0.008 0.282±0.014 0.096±0.010

Table 4

One-error of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.222±0.019 0.230±0.023 0.228±0.029 0.235±0.030 0.176±0.022
emotions 0.244±0.049 0.253±0.070 0.263±0.067 0.318±0.057 0.216±0.085
scene 0.179±0.080 0.250±0.027 0.219±0.029 0.821±0.031 0.226±0.034

Table 5

Coverage of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.458±0.022 0.514±0.018 0.447±0.014 0.456±0.019 0.516±0.015
emotions 0.284±0.033 0.295±0.027 0.300±0.019 0.300±0.022 0.322±0.022
scene 0.086±0.010 0.089±0.009 0.078±0.010 0.374±0.024 0.091±0.008

Table 6

Ranking loss of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.171±0.014 0.200±0.013 0.166±0.015 0.171±0.015 0.285±0.022
emotions 0.147±0.030 0.156±0.034 0.163±0.022 0.173±0.020 0.233±0.040
scene 0.086±0.012 0.089±0.011 0.076±0.012 0.434±0.026 0.135±0.013

Table 7

Average precision of five multi-label algorithms on three datasets(mean±std)"

数据集 Algorithm
TML_LC BSVM ML-KNN BPMLL ECC
yeast 0.761±0.016 0.794±0.019 0.765±0.021 0.754±0.020 0.728±0.019
emotions 0.817±0.031 0.807±0.037 0.799±0.031 0.779±0.027 0.796±0.042
scene 0.871±0.017 0.849±0.016 0.869±0.017 0.445±0.018 0.852±0.016
1 ZHANG Minlin , ZHOU Zhihua . A review on multi-label learning algorithms[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26 (8): 1819- 1837.
doi: 10.1109/TKDE.2013.39
2 TROHIDIS K, TSOUMAKAS G, KALLIRIS G, et al. Multi-label classification of music into emotions[C]//9th International Conference on Music Information Retrieval. Philadelphia Springer, 2008: 325-330.
3 LIU S M , CHEN J H . A multi-label classification based approach for sentiment classification[J]. Expert Systems with Applications, 2015, 42 (3): 1083- 1093.
doi: 10.1016/j.eswa.2014.08.036
4 HUANG Shu, PENG Wei, LI Jingxuan, et al. Sentiment and topic analysis on social media: a multi-task multi-label classification approach[C]//Proceedings of the 5th Annual ACM Web Science Conference. Paris: AC, 2013.
5 WU Baoyuan , LYU S , HU Baogang , et al. Multi-label learning with missing labels for image annotation and facial action unit recognition[J]. Pattern Recognition, 2015, 48 (7): 2279- 2289.
doi: 10.1016/j.patcog.2015.01.022
6 QIN Jianzhao , YUNG N H C . Feature fusion within local region using localized maximum-margin learning for scene categorization[J]. Pattern Recognition, 2012, 45 (4): 1671- 1683.
doi: 10.1016/j.patcog.2011.09.027
7 BARUTCUOGLU Z , SCHAPIRE R E , TROYANSKAYA O G . Hierarchical multi-label prediction of gene function[J]. Bioinformatics, 2006, 22 (7): 830- 836.
doi: 10.1093/bioinformatics/btk048
8 JIANG J Q , MCQUAY L J . Predicting protein function by multi-label correlated semi-supervised learning[J]. IEEE/ACM Transactions on Computational Biology & Bioinformatics, 2012, 9 (4): 1059- 1069.
9 SONG Yang, ZHANG Lu, GILES C L. A sparse gaussian processes classification framework for fast tag suggestions[C]//Proceedings of the 17th ACM Conference on Information and Knowledge Management. Napa Valley: ACM, 2008: 93-102.
10 OZONAT K M, YOUNG D. Towards a universal marketplace over the web: statistical multi-label classification of service provider forms with simulated annealing[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris: ACM, 2009: 1295-1304.
11 YAO Yiyu , DENG Xiaofei . Sequential three-way decisions with probabilistic rough sets[J]. Information Sciences, 2010, 180 (3): 341- 353.
12 YU Ying , PEDRYCZ W , MIAO Duoqian . Multi-label classification by exploiting label correlations[J]. Expert Systems with Applications, 2014, 41 (6): 2989- 3004.
doi: 10.1016/j.eswa.2013.10.030
13 刘盾. 三支决策与粒计算[M]. 北京: 科学出版社, 2013.
LIU Dun . Three decisions and granular computing[M]. Beijing: Science Press, 2013.
14 YAO Yiyu. Granular computing and sequential three-way decisions[C]//RSKT 2013: Rough Sets and Knowledge Technology, Halifax: Springer, 2013: 16-27.
15 ZHENG Taoyu, ZHENG Zhiyun, TANG Shiping, et al. Query expansion for answer document retrieval in chinese question answering system[C]// International Conference on Machine Learning & Cybernetics. Guangzhou: Springer, 2005: 278-284.
16 YAO Jingtao , LI Huaxiong , PETERS G . Decision-theoretic rough sets and beyond[J]. International Journal of Approximate Reasoning, 2014, 55 (1): 99- 100.
doi: 10.1016/j.ijar.2013.09.022
17 BOUTELL M , LUO Jiebo , SHEN Xipeng , et al. Learning multi-label scene classification[J]. Pattern Recognition, 2004, 37 (9): 1757- 1771.
doi: 10.1016/j.patcog.2004.03.009
18 ZHANG Minling , ZHOU Zhihua . ML-KNN: A lazy learning approach to multi-label learning[J]. Pattern Recognition, 2007, 40 (7): 2038- 2048.
doi: 10.1016/j.patcog.2006.12.019
19 HUANG Jun, LI Guorong, HUANG Qingming, et al. Learning label specific features for multi-label classification[C]//IEEE International Conference on Data Mining. Barcelona: IEEE, 2016: 181-190.
20 KONG D G, DING C, HUANG H, et al. Multi-label ReliefF and F-statistic feature selections for image annotation[C]//Computer Vision and Pattern Recognition. Providence: IEEE, 2012: 2352-2359.
21 READ J , PFAHRINGER B , HOLMES G , et al. Classifier chains for multi-label classification[M]. Berlin: Springer, 2009: 254- 269.
22 READ J , PFAHRINGER B , HOLMES G , et al. Classifier chains for multi-label classification[J]. Machine Learning, 2011, 85 (3): 333- 359.
23 ZHANG Minlin, ZHANG Kun. Multi-label learning by exploiting label dependency[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington: ACM, 2010: 999-1008.
24 TSOUMAKAS G , KATAKIS I . Multi-label classification: an overview[J]. International Journal of Data Warehousing and Mining, 2007, (3): 1- 13.
25 CHEN Weijie , SHAO Yuanhai , LI Chunna , et al. MLTSVM: a novel twin support vector machine to multi-label learning[J]. Pattern Recognition, 2016, 52: 61- 74.
doi: 10.1016/j.patcog.2015.10.008
26 PLATT J . Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods[J]. Advances in Large Margin Classifiers, 1999, 10 (3): 61- 74.
27 ZHANG Minlin , ZHOU Zhihua . Multilabel neural networks with applications to functional genomics and text categorization[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 18 (10): 1338- 1351.
28 ELISSEEFF A , WESTON J . Kernel methods for multi-labelled classification and categorical regression problems[J]. Advances in Neural Information Processing Systems, 2002, 14: 681- 687.
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