Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (3): 18-24.doi: 10.6040/j.issn.1672-3961.0.2021.318

Previous Articles     Next Articles

K-nearest neighbor based partial label learning algorithm for class imbalanced data

WANG Li, YU Mingqian, LIU Wenpeng, ZHOU Yu, ZHENG Ruirui, HE Jianjun*   

  1. College of Information and Communication Engineering, Dalian Minzu University, Dalian 116000, Liaoning, China
  • Published:2022-06-23

CLC Number: 

  • TP391
[1] COUR T, SAPP B, TASKAR B. Learning from partial labels[J]. The Journal of Machine Learning Research, 2011, 12: 1501-1536.
[2] COUR T, SAPP B, JORDAN C, et al. Learning from ambiguously labeled images[C] //Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA:IEEE, 2009: 919-926.
[3] JIN R, GHAHRAMANI Z. Learning with multiple labels[J].Advances in Neural Information Processing Systems, 2002, 3(2):921-928.
[4] COME E, OUKHELLOU L. Learning from partially supervised data using mixture models and belief functions[J]. Pattern Recognition, 2009, 42(3):334-348.
[5] LIU L P, DIETTERICH T G.A Conditional multinomial mixture model for superset label learning[C] //Proceedings of the Advances in Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc, 2012:557-565.
[6] LIU L P, DIETTERICH T G. Leainability of the superset label learning problem[C] //Proceedings of the 31st International Conference on Machine Learning. Beijing, China: JMLR, 2014:1629-1637.
[7] HULLERMERIER E, BERINGER J. Learning from ambiguously labeled examples[J]. Intelligent Data Analysis, 2006, 10(5): 419-439.
[8] 周斌斌.基于问题转换的偏标记学习算法研究[D].南京:东南大学自动化系,2017. ZHOU Binbin. Research on partial label learning algorithm[D]. Nanjing: School of Automation, Southeast University, 2017.
[9] ZENG Z, XIAO S, JIA K, et al. Learning by associating ambiguously labeled images[C] //Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.California,USA:IEEE, 2013:708-715.
[10] JIE L, ORABONA F. Learning from candidate labeling sets[C] //Advances in Neural Information Processing Systems 23. Cambridge, USA:MIT Press, 2010:1504-1512.
[11] ZHANG M L, ZHOU B B, LIU X Y. Partial label learning via feature-aware disambiguation[C] //Proceedings of the 22nd ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2016:1335-1344.
[12] CHEN Y C, PATEL V M, CHELLAPPA R, et al. Ambiguously labeled learning using dictionaries[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(12): 2076-2088.
[13] GONG C, LIU T, TANG Y, et al. A regularization approach for instance-based superset label learning[J]. IEEE Transactions on Cybernetics, 2017, 48(3): 967-978.
[14] LUO J, ORABONA F. Learning from candidate labeling sets[J]. Advances in Neural Information Processing Systems, 2010, 23(3):295-299.
[15] BEYGELZIMER A, LANGFORD J. The offset tree for learning with partial labels[C] //Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2009: 129-138.
[16] C(^overO)ME E, OUKHELLOU L, DENOEUX T, et al. Learning from partially supervised data using mixture models and belief functions[J]. Pattern Recognition, 2009, 42(3): 334-348.
[17] TANG C Z, ZHANG M L. Confidence-rated discriminative partial label learning[C] //Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI Press, 2017: 2611-2617.
[18] ZHOU Y, HE J, GU H. Partial label learning via gaussian processes[J]. Cybernetics, IEEE Transactions on, 2017, 47(12):4443-4450.
[19] YE Zhifei, WEN Yimin, LÜ Baoliang. A survey of imbalanced pattern classification problems[J]. China Association of Artificial Intelligence Transcation Intelligent Systems, 2009, 4(2): 148-156.
[20] LI S, WANG Z, ZHOU G, et al. Semi-supervised learning for imbalanced sentiment classification[J]. Plor, 2011, 4(l):1826-1831.
[21] CHEN E, LIN Y, XIONG H, et al. Exploiting probabilistic topic models to improve text categorization under class imbalance[J].Information Processing & Management, 2011, 47(2): 202-214.
[22] LUSA L, BLAGUS R. The class-imbalance problem for high-dimensional class prediction[C] //Proceedings of 2012 11th International Conference on Machine Learning and Applications. Boca Raton, FL, USA: IEEE, 2012:123-126.
[23] WANG J, ZHANG M L. Towards mitigating the class-imbalance problem for partial label learning[C] //Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: Association for Computing Machinery, 2018: 2427-2436.
[24] 周瑜,顾宏.面向不平衡数据的逻辑回归偏标记学习算法[J].大连理工大学学报,2017,57(2):184-188. ZHOU Yu, GU Hong. Logistic regression biased labeling learning algorithm for imbalanced data[J]. Journal of Dalian University of Technology, 2017, 57(2):184-188.
[25] HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning[J]. Journal of the Royal Statistical Society, 2004, 167(1):192.
[26] HAN H, WANG W Y, MAO B H. Borderline-smote: a new over-sampling method in imbalanced data sets learning[C] //Proceedings of the 2005 International Conference on Advances in Intelligent Computing: Volume Part I(ICIC'05). Berlin, Germany: Springer-Verlag, 2005: 878-887.
[27] JING P, HEISTERKAMP D R, DAI H K. Adaptive quasiconformal kernel nearest neighbor classification[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(5):656-661.
[28] PANIS G, LANITIS A. An overview of research activities in facial age estimation using the FG-NET aging database[C] //Proceedings of Lecture Notes in Computer Science(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).Zurich, Switzerland: Lecture Notes in Computer Science, 2015, 8926: 737-750.
[29] BRIGGS F, FERN X Z, RAICH R. Rank-loss support instance machines for MIML instance annotation[C] //Proceedings of the ACM SIGKDD International Con-ference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2012: 534-542.
[1] DENG Bin, ZHANG Zongbao, ZHAO Wenmeng, LUO Xinhang, WU Qiuwei. Cloud-edge collaborative and graph neural network based load forecasting method for electric vehicle charging stations [J]. Journal of Shandong University(Engineering Science), 2025, 55(5): 62-69.
[2] LI Erchao, ZHANG Zhizhao. Online dynamic demand vehicle routing planning [J]. Journal of Shandong University(Engineering Science), 2024, 54(5): 62-73.
[3] YANG Jucheng, WEI Feng, LIN Liang, JIA Qingxiang, LIU Jianzheng. A research survey of driver drowsiness driving detection [J]. Journal of Shandong University(Engineering Science), 2024, 54(2): 1-12.
[4] XIAO Wei, ZHENG Gengsheng, CHEN Yujia. Named entity recognition method combined with self-training model [J]. Journal of Shandong University(Engineering Science), 2024, 54(2): 96-102.
[5] Gang HU, Lemeng WANG, Zhiyu LU, Qin WANG, Xiang XU. Importance identification method based on multi-order neighborhood hierarchical association contribution of nodes [J]. Journal of Shandong University(Engineering Science), 2024, 54(1): 1-10.
[6] Jiachun LI,Bowen LI,Jianbo CHANG. An efficient and lightweight RGB frame-level face anti-spoofing model [J]. Journal of Shandong University(Engineering Science), 2023, 53(6): 1-7.
[7] Yujiang FAN,Huanhuan HUANG,Jiaxiong DING,Kai LIAO,Binshan YU. Resilience evaluation system of the old community based on cloud model [J]. Journal of Shandong University(Engineering Science), 2023, 53(5): 1-9, 19.
[8] Ying LI,Jiankun WANG. The classification of mild cognitive impairment based on supervised graph regularization and information fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 65-73.
[9] WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao, JIN Di. Poisson-gamma topic model of describing multiple underlying relationships [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 51-60.
[10] YU Mingjun, DIAO Hongjun, LING Xinghong. Online multi-object tracking method based on trajectory mask [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 61-69.
[11] LIU Xing, YANG Lu, HAO Fanchang. Finger vein image retrieval based on multi-feature fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 118-126.
[12] LIU Fangxu, WANG Jian, WEI Benzheng. Auxiliary diagnosis algorithm for pediatric pneumonia based on multi-spatial attention [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 135-142.
[13] YU Yixuan, YANG Geng, GENG Hua. Multimodal hierarchical keyframe extraction method for continuous combined motion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 42-50.
[14] HUANG Huajuan, CHENG Qian, WEI Xiuxi, YU Chuchu. Adaptive crow search algorithm with Jaya algorithm and Gaussian mutation [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 11-22.
[15] ZHANG Hao, LI Ziling, LIU Tong, ZHANG Dawei, TAO Jianhua. A technology prediction model based on fuzzy Bayesian networks with sociological factors [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 23-33.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] WANG Su-yu,<\sup>,AI Xing<\sup>,ZHAO Jun<\sup>,LI Zuo-li<\sup>,LIU Zeng-wen<\sup> . Milling force prediction model for highspeed end milling 3Cr2Mo steel[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 1 -5 .
[2] ZHANG Yong-hua,WANG An-ling,LIU Fu-ping . The reflected phase angle of low frequent inhomogeneous[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 22 -25 .
[3] LI Kan . Empolder and implement of the embedded weld control system[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 37 -41 .
[4] SHI Lai-shun,WAN Zhong-yi . Synthesis and performance evaluation of a novel betaine-type asphalt emulsifier[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2008, 38(4): 112 -115 .
[5] KONG Xiang-zhen,LIU Yan-jun,WANG Yong,ZHAO Xiu-hua . Compensation and simulation for the deadband of the pneumatic proportional valve[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 99 -102 .
[6] LAI Xiang . The global domain of attraction for a kind of MKdV equations[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(1): 87 -92 .
[7] YU Jia yuan1, TIAN Jin ting1, ZHU Qiang zhong2. Computational intelligence and its application in psychology[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2009, 39(1): 1 -5 .
[8] CHEN Rui, LI Hongwei, TIAN Jing. The relationship between the number of magnetic poles and the bearing capacity of radial magnetic bearing[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2018, 48(2): 81 -85 .
[9] LI Ke,LIU Chang-chun,LI Tong-lei . Medical registration approach using improved maximization of mutual information[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 107 -110 .
[10] JI Tao,GAO Xu/sup>,SUN Tong-jing,XUE Yong-duan/sup>,XU Bing-yin/sup> . Characteristic analysis of fault generated traveling waves in 10 Kv automatic blocking and continuous power transmission lines[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2006, 36(2): 111 -116 .