An “outside the box” solution for imbalanced data classification

Classification from the imbalanced data is a common yet unresolved problem. Our scientists, Hubert Jegierski and dr. Stanisław Saganowski, introduce an “outside the box” solution to this problem. They suggest enriching the imbalanced data set with observations from other external data sets. They propose three enrichment scenarios: random, greedy, and supervised. Based on extensive research, they have proved that the approach surpasses the existing solutions. Their enrichment approach is especially helpful for small data sets, for which it improves classification efficiency by up to 66%.
The method’s details and the full results have been just published in the IEEE Access journal:
Feel free to read the full paper, and congratulations to our young scientists!