Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4944631 | Information Sciences | 2017 | 40 Pages |
Abstract
The outcomes of the comparative studies concerning imbalancing problem with regard to our dataset showed that the highest efficiency was achieved while using the synthetic minority over-sampling technique and RandomForest classifier. As far as the optimal balancing level is concerned, we empirically determined that 300% oversampling with the synthetic minority over-sampling method combined with edited nearest neighbours undersampling allowed to gain the required precision of classification.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
M. Bach, A. Werner, J. Żywiec, W. Pluskiewicz,