2018/04/19 by Ludmila I. Kuncheva, Álvar Arnaiz‐González, Álvar Arnaiz-González +6
Computer Science · Mathematics · #62H30 #Advanced Statistical Methods and Models #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #cs.CV #cs.LG #msc:62H30 #stat.ML
paper · pdf · doi:10.48550/arxiv.1804.07155
11 pages, 7 figures
arxiv created 2018/04/19 · openalex publication_date 2018/04/19 · arxiv updated 2018/04/20 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.