"Multi-label Learning by Hyperparameters Calibration for Treating Class Imbalance"

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Book	Author(s)	Year	Publisher
Multi-Label Dimensionality Reduction	Liang Sun; Shuiwang Ji; Jieping Ye	2013	Routledge
A method for the calibration of class 2 and class 4 standards of mass	Nabil A. Bibawy	1989	U.S. Dept. of the Interior, Bureau of Mines
Low Power Wireless Receivers for IoT Applications with Multi-band Calibration Algorithms	Michael W. Rawlins	1st ed. 2021	Springer International Publishing
Multi Asset Class Investment Strategy	Guy Fraser-Sampson	2006	John Wiley & Sons
Multi asset class investment strategy /	Fraser-Sampson, Guy.	c2006.	Wiley,
Multi-Objective Machine Learning	Yaochu Jin	2006	Springer Berlin Heidelberg
Label-free learning: supporting learners with disabilities	Charlotte Hendrick Keefe	1996	Stenhouse Publishers
Iterative Learning Control for Multi-agent Systems Coordination	Shiping Yang; Jian-Xin Xu; Xuefang Li; Dong Shen	2017	Wiley-IEEE Press
calibration	Senthil
Blue label dahlias for 1929	Wayland Dahlia Gardens	1929
Multivariate calibration	Harald Martens; Tormod Næs	1989	Wiley
Fluent Bodies: Ayurvedic Remedies for Postcolonial Imbalance	Jean M. Langford	2002	Duke University Press
Fluent bodies: Ayurvedic remedies for postcolonial imbalance /	Langford, Jean.	2002.	Duke University Press,
Learning Representation for Multi-View Data Analysis: Models and Applications	Zhengming Ding; Handong Zhao; Yun Fu	1st ed. 2019	Springer International Publishing
Creating Multi-sensory Environments: Practical Ideas for Teaching and Learning	Christopher Davies	2019	Routledge
Joy of learning: creative individualization for deaf multi-handicapped children	Peggy Eaton	1976	Dormac
Treating Youth Who Sexually Abuse: An Integrated Multi-Component Approach	Stephen Lundrigan	2001	Routledge
Treating youth who sexually abuse: an integrated multi-component approach /	Lundrigan, Paul Stephen.	©2001.	Haworth Press,
Treating Youth Who Sexually Abuse: An Integrated Multi-Component Approach	Paul Stephen Lundrigan	May 2001	Haworth Press
Viscometry for Liquids: Calibration of Viscometers	S. V. Gupta	2014	Springer International Publishing
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Multi-label Learning by Hyperparameters Calibration for Treating Class Imbalance	Andrés Felipe Giraldo-Forero; Andrés Felipe Cardona-Escobar; Andrés Eduardo Castro-Ospina	Hybrid Artificial Intelligent Systems: 13th International Conference, HAIS 2018, Oviedo, Spain, June 20-22, 2018, Proceedings	Francisco Javier de Cos Juez; José Ramón Villar; Enrique A. de la Cal; Álvaro Herrero; Héctor Quintián; José António Sáez; Emilio Corchado	1st ed. 2018	Springer International Publishing
Multi-label Learning	Zhi-Hua Zhou; Min-Ling Zhang	Encyclopedia of Machine Learning and Data Mining	Claude Sammut; Geoffrey I. Webb	2nd ed. 2017	Springer US
Multi-label Learning	Sarah Vluymans	Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods	Sarah Vluymans	1st ed. 2019	Springer International Publishing
Multi-label Learning	Zhi-Hua Zhou; Min-Ling Zhang	Encyclopedia of Machine Learning and Data Science	Dinh Phung; Geoffrey I. Webb; Claude Sammut	0th ed. 2020	Springer US
Multi-class multi-label classification		Deep Learning Essentials: Your hands-on guide to the fundamentals of deep learning and neural network modeling (English Edition)	Wei Di, Anurag Bhardwaj, Jianing Wei	2018	Packt Publishing
Discriminative Multi-label Model Reuse for Multi-label Learning	Yi Zhang; Zhecheng Zhang; Yinlong Zhu; Lei Zhang; Chongjun Wang	Web and Big Data: 4th International Joint Conference, APWeb-WAIM 2020, Tianjin, China, September 18-20, 2020, Proceedings, Part I	Xin Wang; Rui Zhang; Young-Koo Lee; Le Sun; Yang-Sae Moon	1st ed. 2020	Springer International Publishing
Multi-label Learning by Exploiting Imbalanced Label Correlations	Shiqiao Gu; Liu Yang; Yaning Li; Hui Li	PRICAI 2021: Trends in Artificial Intelligence: 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8–12, 2021, Proceedings, Part II	Duc Nghia Pham; Thanaruk Theeramunkong; Guido Governatori; Fenrong Liu	1st ed. 2021	Springer International Publishing
Multi-instance Multi-label Learning by Extreme Learning Machine	Chenguang Li; Ying Yin; Yuhai Zhao; Guang Chen; Libo Qin	Proceedings of ELM-2015 Volume 2: Theory, Algorithms and Applications (II)	Jiuwen Cao; Kezhi Mao; Jonathan Wu; Amaury Lendasse	1st ed. 2016	Springer International Publishing
Ensemble Methods for Class Imbalance Learning		Imbalanced Learning: Foundations, Algorithms, and Applications	Haibo He; Yunqian Ma	2013	Wiley-IEEE Press
Assessments Metrics for Multi-class Imbalance Learning: A Preliminary Study	R. Alejo; J. A. Antonio; R. M. Valdovinos; J. H. Pacheco-Sánchez	Pattern Recognition: 5th Mexican Conference, MCPR 2013, Queretaro, Mexico, June 26-29, 2013. Proceedings	Jesús Ariel Carrasco-Ochoa; Jose Francisco Martinez-Trinidad; Joaquin Salas Rodriguez; Gabriella Sanniti di Baja	2013	Springer Berlin Heidelberg
Multi-label Ensemble Learning	Chuan Shi; Xiangnan Kong; Philip S. Yu; Bai Wang	Machine Learning and Knowledge Discovery in Databases, Part III: European Conference, ECML PKDD 2010, Athens, Greece, September 5-9, 2011, Proceedings, Part III	Dimitrios Gunopulos; Thomas Hofmann; Donato Malerba; Michalis Vazirgiannis	2011	Springer Berlin Heidelberg
Learning Tree Structure of Label Dependency for Multi-label Learning	Bin Fu; Zhihai Wang; Rong Pan; Guandong Xu; Peter Dolog	Advances in Knowledge Discovery and Data Mining, Part I: 16th Pacific-Asia Conference, PAKDD 2012, Kuala Lumpur, Malaysia, May 29-June1, 2012, Proceedings, Part I	Pang-Ning Tan; Sanjay Chawla; Chin Kuan Ho; James Bailey	2012	Springer Berlin Heidelberg
Metric Learning for Multi-label Classification	Marco Brighi; Annalisa Franco; Dario Maio	Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, S+SSPR 2020, Padua, Italy, January 21–22, 2021, Proceedings	Andrea Torsello; Luca Rossi; Marcello Pelillo; Battista Biggio; Antonio Robles-Kelly	1st ed. 2021	Springer International Publishing
Class-Similarity Based Label Smoothing for Confidence Calibration	Chihuang Liu; Joseph JaJa	Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV	Igor Farkaš; Paolo Masulli; Sebastian Otte; Stefan Wermter	1st ed. 2021	Springer International Publishing
Active Multi-Instance Multi-Label Learning	Robert Retz; Friedhelm Schwenker	Analysis of Large and Complex Data	Adalbert F.X. Wilhelm; Hans A. Kestler	1st ed. 2016	Springer International Publishing
Class Imbalance and Active Learning		Imbalanced Learning: Foundations, Algorithms, and Applications	Haibo He; Yunqian Ma	2013	Wiley-IEEE Press
Label Correlation Propagation for Semi-supervised Multi-label Learning	Aritra Ghosh; C. Chandra Sekhar	Pattern Recognition and Machine Intelligence: 7th International Conference, PReMI 2017, Kolkata, India, December 5-8, 2017, Proceedings	B. Uma Shankar; Kuntal Ghosh; Deba Prasad Mandal; Shubhra Sankar Ray; David Zhang; Sankar K. Pal	1st ed. 2017	Springer International Publishing
Multi-class Multi-label Classification for Cooking Activity Recognition	Shkurta Gashi; Elena Di Lascio; Silvia Santini	Human Activity Recognition Challenge	Md Atiqur Rahman Ahad; Paula Lago; Sozo Inoue	1st ed. 2021	Springer Nature Singapore
Learning Validation for Lung CT Images by Multivariable Class Imbalance	Dao Nam Anh; Nguyen The Hoang	Frontiers in Intelligent Computing: Theory and Applications: Proceedings of the 7th International Conference on FICTA (2018), Volume 1	Suresh Chandra Satapathy; Vikrant Bhateja; Bao Le Nguyen; Nhu Gia Nguyen; Dac-Nhuong Le	1st ed. 2020	Springer Nature Singapore
Class Imbalance		Designing Machine Learning Systems	Chip Huyen	2022	O'Reilly Media, Inc.
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