The skyline of counterfactual explanations for machine learning decision models

• المؤلفون الرئيسيون: Wang, Yongjie, Ding, Qinxu, Wang, Ke, Liu, Yue, Wu, Xingyu, Wang, Jinglong, Liu, Yong, Miao, Chunyan
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2022
• الموضوعات: Engineering::Computer science and engineering; Counterfactual Explanations; Multi-Objective Optimization
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/156946
• المؤسسة: Nanyang Technological University

Counterfactual explanations are minimum changes of a given input to alter the original prediction by a machine learning model, usually from an undesirable prediction to a desirable one. Previous works frame this problem as a constrained cost minimization, where the cost is defined as L1/L2 distance...