The Multiclass ROC Front method for cost-sensitive classification
作者：
Highlights：
• We propose a new method for multiclass cost-sensitive classification when misclassification costs are unknown during training.
• It is based on a multi-model approach and can suit to any cost-sensitive environment in prediction.
• It makes use of ROC-based multi-objective optimization algorithms.
• The method is compared to a cost-insensitive method and a state-of-the-art cost-sensitive optimization method.
• It outperforms both methods for most of the datasets tested.
摘要
Highlights•We propose a new method for multiclass cost-sensitive classification when misclassification costs are unknown during training.•It is based on a multi-model approach and can suit to any cost-sensitive environment in prediction.•It makes use of ROC-based multi-objective optimization algorithms.•The method is compared to a cost-insensitive method and a state-of-the-art cost-sensitive optimization method.•It outperforms both methods for most of the datasets tested.
论文关键词：Multiclass classification,Cost-sensitive classification,ROC optimization,Multi-objective optimization,SVM classifier
论文评审过程：Received 13 January 2015, Revised 15 September 2015, Accepted 12 October 2015, Available online 22 October 2015, Version of Record 24 December 2015.
论文官网地址：https://doi.org/10.1016/j.patcog.2015.10.010
