.png){kind=icon}
Return
What makes multi-class imbalanced problems difficult? An experimental study
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2022.116962.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-class imbalanced classification is more difficult and less frequently studied than its binary counterpart. Moreover, research on the causes of the difficulty of multi-class imbalanced data is quite limited and insufficient. Therefore, we experimentally study the impact of various multi-class imbalanced difficulty factors on the performance of three popular classifiers. The results demonstrated a strong influence of the class overlapping with the extent of its impact related to the types of overlapped classes. In particular, overlapping between minority and majority classes was more difficult than the others. The type of the class size configuration turned out to be another important factor, highlighting the special role of the configurations with classes of intermediate sizes. The obtained results could support studying the nature of the multi-class imbalanced data as well as the development of new methods for improving classifiers.
Keywords:
Imbalanced data
Classification
Learning from multiple classes
Data difficulty factors
Journal
IF:
7.5
Papers:
3.0W
Citations:
10.2W
Organization
Cited Papers
ShareSaveTo combat multi-class imbalanced problems by means of over-sampling and boosting techniques.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
ShareSaveA dynamic over-sampling procedure based on sensitivity for multi-class problemsPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveShareSaveShareSave
ShareSave
View more