A learning strategy for highly imbalanced classification
Tong Liu, Yongquan Liang, Weijian Ni · 2011
This paper describes a new learning strategy on the problem of classification on overlapped and imbalanced training set. We devise an adaptive scheme for minority generating; with data cleaning of majority, new clusters are drawn to increasingly focus on the combination of new minority samples. Inspired by the essence of SVM, we extract the most informative SVs for training. An empirical study compares the performance of our strategy against traditional classification approaches on the benchmark data sets, and experimental results show that our learning strategy not only inherent data distribution, but also improve classification effectiveness and efficiency.