A Weighted kNN Classification Method for Partial Labeling

doi:10.3969/j.issn.1006-2475.2015.12.007

Abstract

Abstract:

As one of the important weaklysupervised machine learning frameworks, partial label learning is different from traditional supervised learning. Under this framework,

an instance might be associated with a set of candidate labels among which only one is valid. The knearest neighbor method is simple but effective for classification. In this

paper, we propose a weighted kNN partial labeling classification method. Firstly, for an unseen instance, it will try to find k nearest neighbors of the unseen instance in

training set. Secondly, the weight of every nearest neighbor is determined by solving a quadratic programming problem. Lastly, the label of the unseen instance is decided in

accordance with the principle of decision by majority. Extensive experiments show that the proposed method can effectively improve the generalization performance of the learning

system.

Key words: machine learning, data mining, partial label learning, k-nearest neighbor, weight estimation

CLC Number:

TP181

LIANG Weichao， SONG Bin. A Weighted kNN Classification Method for Partial Labeling[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.12.007.

References ［17］

1］	Grandvalet Y. Logistic regression for partial labels ［C］// Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in
	KnowledgeBased Systems. Annecy. 2002:1935-1941.
［2］	Cour T, Sapp B, Taskar B. Learning from partial labels［J］. Journal of Machine Learning Research, 2011,12(5):1501-1536.
［3］	张敏灵. 偏标记学习研究综述［J］. 数据采集与处理, 2015,30(1):77-87.
［4］	Jin R, Ghahramani Z. Learning with multiple labels［C］// Advances in Neural Information Processing System. 2003:897-904.
［5］	Nguyen N, Caruana R. Classification with partial labels［C］// Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2008:381
	-389.
［6］	Liu Liping, Dietterich T G. A conditional multinomial mixture model for superset label learning［C］// Advances in Neural Information Processing System. 2012:557565.
［7］	Hullermeier E, Beringer J. Learning from ambiguously labeled examples［J］. Intelligent Data Analysis, 2006,10(5):419-439.
［8］	Cover T M, Hart P E. Nearest neighbor pattern classification［J］. IEEE Transactions on Information Theory, 1967,13(1):21-27.
［9］	Duda R O, Hart P E. Pattern Classification and Scene Analysis［M］. New York: Wiley, 1973.
［10］	Wu Xindong, Kumar V, Quinlan J R, et al. Top 10 algorithms in data mining［J］. Knowledge and Information Systems, 2008,14(1):1-37.
［11］	Duduni S A. The distanceweighted knearest neighbor rule［J］. IEEE Transactions on Systems, Man, and Cybernetics, 1976,6(2):325-327.
［12］	Macleod J E S, Luk A, Titterington D M. A reexamination of the distanceweighted knearest neighbor classification rule［J］. IEEE Transactions on Systems, Man, and
	Cybernetics, 1987,17(4):689696.
［13］	Zuo Wangmeng, Zhang D, Wang Kuanquan. On kernel differenceweighted knearest neighbor classification［J］. Pattern Analysis and Applications, 2008,11(34):247-
	257.
［14］	Lichman M. UCI Machine Learning Repository［EB/OL］. http://archive.ics.uci.edu/ml, 2013-04-04.
［15］	Zhang Minling. Solving the partial label learning problem: An instancebased approach［C］// Proceedings of the 24th International Joint Conference on Artificial
	Intelligence. 2015:4048-4054.
［16］	Zhang Minling. Disambiguationfree partial label learning［C］// Proceedings of the 14th SIAM International Conference on Data Mining. 2014:37-45.
［17］	Chen Yichen, Patel V M, Chellappa R, et al. On kernel differenceweighted knearest neighbor classification［J］. IEEE Transactions on Information Forensics and
	Security, 2014,12(9):2076-2088.

[1]	JIA Xiao-yao, . Breast Cancer Prediction and Feature Analysis Model Based on CatBoost and SHAP [J]. Computer and Modernization, 2023, 0(10): 32-38.
[2]	XIE Shi-bin, LIU Meng-chi, TANG Shi-qi, ZHOU Rui-ping, . A Temporal Convolutional Knowledge Tracking Model Based on#br# Multiple Feature Extraction#br# [J]. Computer and Modernization, 2023, 0(07): 25-29.
[3]	LIU Pei. Medical Insurance Fee Control System Based on Data Mining [J]. Computer and Modernization, 2023, 0(06): 89-94.
[4]	ZHANG Yun, BAI Kai-feng, WANG Xing, CANG Tian, ZHOU Tong, DUAN Jin-wen, SU Han. Review of Electricity Theft Detection in Smart Grid Environment [J]. Computer and Modernization, 2023, 0(03): 60-65.
[5]	WANG Shao-hua, OUYANG Hui-dan, SUN Dan, WANG Kang, WU Hong-ping, ZHONG Xun, CHU Xing-ping, YANG Song-tao. Association Rule Mining of Undergraduate Physical Test Items Based on Apriori Algorithm [J]. Computer and Modernization, 2023, 0(03): 66-70.
[6]	SHI Zhi-wei, WU Zhi-feng, ZHANG Zhe. Stock Volatility Prediction of LightGBM-GRU Model under Corrective Learning Strategy [J]. Computer and Modernization, 2023, 0(01): 95-102.
[7]	SONG Xiao-li, ZHANG Yong-bo, ZHANG Pei-ying. Anomaly Detection of Student Consumption Data Based on Semi-supervised Learning [J]. Computer and Modernization, 2022, 0(12): 13-17.
[8]	GUAN Yun-peng, LIU Yu-long. Categorical Data Clustering Based on Extraction of Associations from Co-association Matrix [J]. Computer and Modernization, 2022, 0(11): 1-8.
[9]	DUAN Gui-qin, ZOU Chen-song. Occupational Competence Evaluation Model Based on Affinity Propagation Clustering [J]. Computer and Modernization, 2022, 0(05): 21-27.
[10]	LENG Tao , . A Survey of Encrypted Traffic Classification Based on Deep Learning [J]. Computer and Modernization, 2021, 0(08): 112-120.
[11]	DENG Zi-yun , . A Screening Method of Machine Learning Model for Auxiliary Diagnosis [J]. Computer and Modernization, 2021, 0(03): 88-93.
[12]	LIU Bin, LI Xiao. Application of Multi-label Classification Based on Digital Content Preference [J]. Computer and Modernization, 2021, 0(02): 45-50.
[13]	YANG Lin, BAI Zhao, KOU Yong-gang. Analysis of Airline Customer Churn by Random Forest Algorithm Based on RFM Model [J]. Computer and Modernization, 2021, 0(01): 100-104.
[14]	GUO Xin, CHEN Ying, ZHANG Ming-huan, ZHANG Xuan, PAN Shu-ming, TANG Lu-jia. Analysis and Prediction of Training Effects of National Disaster Life Support Course with Machine Learning Methods [J]. Computer and Modernization, 2020, 0(12): 61-66.
[15]	LI Ke-xin, LI Jing, SHAO Jia-wei, XIAO Yi. High-dimensional Numerical Anomaly Data Detection Based on Multi-level Sequence Integration [J]. Computer and Modernization, 2020, 0(06): 73-.