DATA FUSION AND CLASSIFICATION WITH IMBALANCED DATASETS BACKGROUND

摘要

Method and system for classification in imbalanced datasets within a supervised classification framework. Bootstrap methodology is modified according to k-Nearest Neighbor sampling weights and adaptive target set size principle, to induce weak classifiers from the bootstrap samples in an iterative procedure that results in a set of weak classifiers. A weighted combination scheme is used to adaptively combine the weak classifiers to a strong classifier that achieves good performance for ah classes (reflected as high values for metrics such as G-mean and F-score) as well as good overall accuracy.