SS01: Ordinal Classification
Victor M. Vargas
David Guijo-Rubio
Pedro A. Gutiérrez
Dept. of Computer Science and Numerical Analysis, University of Córdoba, Spain
Abstract
Ordinal classification is a recent area including those classification tasks in which exist a natural order between the labels, i. e. the target variable presents an ordering relation. For instance, most of health-related problems are covered by this area, since the different stages of a disease could be categorized by {“initial”, “mild”, “medium”, “severe”, “deep”}. In this way, ordinal classification techniques consider the natural order among the classes and penalize regarding the magnitude of the errors. Specific solutions have been recently proposed in the machine learning and pattern recognition literature, resulting in a very promising field. Moreover, it is also a hot topic in the deep learning field, where several approaches have been presented to the literature.
This special session aims to cover a wide range of works and recent advances on ordinal classification. We hope that this session can provide a common forum for researchers and practitioners to exchange their ideas and report their latest finding in the area.
In particular we encourage submissions addressing the following issues:
- Extensions of standard classification methods to ordinal classification (support vector machines, Gaussian processes, discriminant analysis, etc).
- Extensions of deep learning techniques to ordinal classification.
- Threshold models and decomposition methods for ordinal classification.
- Pre-processing methods for ordinal data (data cleaning techniques, feature selection, over-sampling, under-sampling, etc.).
- Evaluation measures for ordinal classification.
- Data preprocessing (feature selection, noise filtering, etc.) for ordinal classification.
- Development of novel ordinal classification frameworks.
- Time series ordinal classification (extensions of standard time series classification methods to time series ordinal classification).
- Applications in 4.0 industry, bio-medicine, renewable energies and environmental issues, economy… and any other real-world problems.
Organizers
MSc. Víctor Manuel Vargas was born in Córdoba, Spain. He received the degree of Computer Engineering from the University of Córdoba, Spain, in 2018. Then, he received his master’s degree in artificial intelligence research at the International University of Menéndez Pelayo, Spain. He is currently doing his Ph.D. thesis in advanced computing, energy and plasma. He is a member of the AYRNA group of the Department of Computer Science and Numerical Analysis of the University of Córdoba. His current research interests include deep neural networks and their applications to image classification on nominal and ordinal problems.
Dr. David Guijo Rubio received the BSc in computer science from the University of Córdoba, Córdoba, Spain, in 2016, the MSc in artificial intelligence from the International University Menendez Pelayo, Madrid, Spain, in 2017 and the Ph.D. degree in computer science and artificial intelligence from the University of Córdoba Córdoba, Spain, in 2021. He is currently a Postdoc Researcher with both the Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain and the School of Computing Sciences of the University of East Anglia, Norwich, United Kingdom. His research interests are in the areas of time series analysis (both supervised and unsupervised), and ordinal classification.
Dr. Pedro Antonio Gutiérrez received the B.S. degree in computer science from the University of Seville, Seville, Spain, in 2006, and the Ph.D. degree in computer science and artificial intelligence from the University of Granada, Granada, Spain, in 2009. He is currently an Assistant Professor with the Department of Computer Science and Numerical Analysis, University of Córdoba, Córdoba, Spain. His research interests are in the areas of supervised learning, evolutionary artificial neural networks, and ordinal classification.