An ensemble and cost-sensitive learning-based root cause diagnosis scheme for wireless networks with spatially imbalanced user data distribution

Abstract

A novel feature extraction method that can tolerate imbalanced user data is proposed. A cost-sensitive SVM assigns different misclassification costs to faults with different severity levels to optimize the cause diagnosis process. The simulation results demonstrate the effectiveness and superiority of the proposed algorithm.