2012/05/02 by Rui Wang, Ke Tang, Wang, Rui +1
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Data Classification #cs.LG
paper · pdf · doi:10.48550/arxiv.1205.0406
6 pages, more materials will be added into the manuscript
arxiv created 2012/05/02 · openalex publication_date 2012/05/02 · arxiv updated 2012/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many studies on the cost-sensitive learning assumed that a unique cost matrix is known for a problem. However, this assumption may not hold for many real-world problems. For example, a classifier might need to be applied in several circumstances, each of which associates with a different cost matrix. Or, different human experts have different opinions about the costs for a given problem. Motivated by these facts, this study aims to seek the minimax classifier over multiple cost matrices. In summary, we theoretically proved that, no matter how many cost matrices are involved, the minimax problem can be tackled by solving a number of standard cost-sensitive problems and sub-problems that involve only two cost matrices. As a result, a general framework for achieving minimax classifier over multiple cost matrices is suggested and justified by preliminary empirical studies.