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MC-SQ and MC-MQ: Ensembles for Multi-class Quantification
  • Abstract
  • Related Work
  • Tables
    • Datasets
    • Binary AE Results
    • Multi-class AE Results
    • Binary NKLD Results
    • Multi-class NKLD Results
  • CD Diagrams
    • AE - Binary Datasets
    • AE - Multi-class Datasets
    • AE - Multi-class Datasets Including OVA Quantifiers
    • NKLD - Binary Datasets
    • NKLD - Multi-class Datasets
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  • Authors
MC-SQ and MC-MQ: Ensembles for Multi-class Quantification
  • Authors:

Zahra Donyavi

She received her M.Sc. degree in Information Technology from the University of Tehran, Tehran, Iran, in 2019. She is currently a Ph.D. student at the Department of Computer Science and Engineering, University of New South Wales (UNSW). Her current research interests include Data Mining, Machine Learning, and, more specifically, Quantification Learning.  

Adriane Serapião 

She received her bachelor's degree in Electrical Engineering (1992) from the Federal University of Goiás (UFG), Brazil, her M.Sc. in Electrical Engineering (1996), and her Ph.D. in Molecular and Structural Biology (2001) from the State University of Campinas (UNICAMP), Brazil. She is a full professor at Paulista State University (UNES) in Rio Claro, Brazil. She is also currently a visiting professor at the Department of Computer Science and Engineering, University of New South Wales (UNSW), in Sydney, Australia. Her research interests include machine learning, data mining, and Bioinspired Computing.

Gustavo Batista

He joined UNSW as an associate professor in 2019 after working for more than ten years at the University of São Paulo, Brazil. His recent research focuses on time series, data stream mining, and quantification. He has published more than 100 papers, and his articles account for more than 13,850 citations, according to Google Scholar.

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