Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4969997 | Pattern Recognition Letters | 2017 | 7 Pages |
Abstract
In this paper, we argue that, although imbalance-ratio is an informative measure for binary problems, it is not adequate for the multi-class scenario due to the fact that, in that scenario, it groups problems with disparate class-imbalance extents under the same numerical value. Thus, in order to overcome this drawback, in this paper, we propose imbalance-degree as a novel and normalised measure which is capable of properly measuring the class-imbalance extent of a multi-class problem. Experimental results show that imbalance-degree is more adequate than imbalance-ratio since it is more sensitive in reflecting the hindrance produced by skewed multi-class distributions to the learning processes.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Jonathan Ortigosa-Hernández, Iñaki Inza, Jose A. Lozano,