Latest Trends Related to Imbalanced Classification Problems in Data Mining: New Approaches and Applications
A special issue of J (ISSN 2571-8800). This special issue belongs to the section "Computer Science & Mathematics".
Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 4293
Special Issue Editor
Interests: data science; data mining; machine learning; classification; regression; data preprocessing; noisy data; imbalanced learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As you know, many real-world classification problems are characterized by a highly imbalanced distribution of samples among the classes. In these problems, one class (the minority class) contains a much smaller number of samples than the other classes (the majority classes). Class imbalance constitutes a difficulty for most learning algorithms which assume an approximately balanced class distribution and are biased toward the learning and recognition of the majority classes. As a result, minority class samples (which are often the most interesting from an application point of view) usually tend to be misclassified.
This Special Issue is focused on papers dealing with the imbalanced classification problem in data mining. Research topics can include but are not limited to:
1) New approaches to deal with imbalanced classification problems;
2) Applications of existing or new methods in the imbalanced classification framework;
3) Studies on class imbalance combined with other problems affecting the data: overlapping, noisy samples, presence of small disjuncts, etc.;
4) Theoretical/experimental reviews of classic and recent approaches in imbalanced classification;
5) Negative and confirmatory results of existing scientific publications related to imbalanced classification.
We cordially welcome research papers and review articles with concise and comprehensive contents related to the topics above. Papers will be subjected to a peer review procedure to ensure the scientific soundness of their content. The review process will also aim for a fast and wide dissemination of the research results of the authors.
Dr. José Antonio Sáez
Guest Editor
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Keywords
- imbalanced learning
- unbalanced learning
- classification
- data preprocessing
- data mining
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