Abstract
Imbalanced classification is one of the most important tasks in machine learning, which has attracted much attention from both academic and industrial communities. Imbalanced classification has a very wide range of real-world applications, most of which are closely related to our daily life, such as medical diagnosis, intrusion detection, anomaly detection, and credit card fraud detection. Imbalanced data exhibits a skewed distribution between its classes. If the class imbalance issue is not well-addressed, classifier are likely to ignore the class of interest which is constituted by a few instances. Computational intelligence is a subfield of artificial intelligence, covering three research branches, including evolutionary computation, fuzzy sets, and artificial neural networks. Computational intelligence techniques have been applied and achieved great contributions to imbalanced classification. In the big data era, the amount of data is growing very rapidly, either increasing in a number of features or instances. This brings further difficulty in constructing effective classifiers when learning from imbalanced data but in return enriches new opportunities.
The aim of this special session is to join the contemporary use of computational intelligence techniques for imbalanced classification . This special session attempts to bring together some of leading experts or researchers from different branches in computational intelligence and offers a forum for them to present their latest research, discuss open questions as well as current advances in imbalanced classification . The session welcomes studies and contributions that introduce novel methods based on different computational intelligence paradigms to imbalanced classification and its applications.
Topics
Topics of interest for this session include but are not limited to:
- Evolutionary computation (e.g. Genetic Algorithms, Genetic Programming and Particle Swarm Optimization, etc.) for imbalanced classification
- Evolutionary computation with sampling methods (including undersampling, oversampling and hybrid sampling) for imbalanced classification
- Evolutionary computation with cost-sensitive learning for imbalanced classification
- Fuzzy sets, Rough sets, Granular computing for imbalanced classification
- Fuzzy rule-based classification systems for imbalanced classification
- Instance selection
- Neural networks for imbalanced classification
- Deep learning for imbalanced classification
- Sampling methods for imbalanced classification
- Instance selection for large-scale imbalanced data
- Cost-sensitive learning for imbalanced classification
- Active learning for imbalanced classification
- Explainable Artificial Intelligence (XAI) for imbalanced classification
- Feature selection/construction/extraction/ranking/analysis for imbalanced classification with high-dimensional data
- Real-world applications of imbalanced classification , e.g., medical data analysis, fault detection, anomaly detection, software defect prediction, and text mining
Organizers
Wenbin Pei, Dalian University of Technology (China)
Bing Xue, Victoria University of Wellington (New Zealand)
Antonio J. Tallón-Ballesteros, University of Huelva (Spain)
Submission
See submission instructions for the conference at https://ideal2024.webs.upv.es/submission/
Special Session Papers Submission Deadline: July 26, 2024