Computer Science ›› 2022, Vol. 49 ›› Issue (9): 33-40.doi: 10.11896/jsjkx.220300158
• Database & Big Data & Data Science • Previous Articles Next Articles
WANG Ming, WU Wen-fang, WANG Da-ling, FENG Shi, ZHANG Yi-fei
CLC Number:
[1]GUNNING D,STEFIK M,CHOI J,et al.XAI-Explainable artificial intelligence[J].Science Robotics,2019,4(37):eaay7120. [2]WACHTER S,MITTELSTADT B,RUSSELL C.Counterfac-tual explanations without opening the black box:Automated decisions and the GDPR[J].Harvard Journal of Law & Techno-logy,2017,31:841-887. [3]PEARL J,MACKENZIE D.The book of why:the new science of cause and effect[M].Basic Books,2018. [4]MOLNAR C.Interpretable machine learning[M].Lulu.com,2020. [5]VELMURUGAN M,OUYANG C,MOREIRA C,et al.Evalua-ting fidelity of explainable methods for predictive process analy-tics[C]//International Conference on Advanced Information Systems Engineering.Cham:Springer,2021:64-72. [6]YUE Z,WANG T,SUN Q,et al.Counterfactual zero-shot and open-set visual recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:15404-15414. [7]MOTHILAL R K,SHARMA A,TAN C.Explaining machine learning classifiers through diverse counterfactual explanations[C]//Proceedings of the 2020 Conference on Fairness,Accountability,and Transparency.2020:607-617. [8]VERMA S,DICKERSON J,HINES K.Counterfactual explanations for machine learning:A review[J].arXiv:2010.10596,2020. [9]KINGMA D P,BA J.Adam:A method for stochastic optimization[J].arXiv:1412.6980,2014. [10]USTUN B,SPANGHER A,LIU Y.Actionable recourse in linearclassification[C]//Proceedings of the Conference on Fairness,Accountability,and Transparency.2019:10-19. [11]POYIADZI R,SOKOL K,SANTOS-RODRIGUEZ R,et al.FACE:feasible and actionable counterfactual explanations[C]//Proceedings of the AAAI/ACM Conference on AI,Ethics,and Society.2020:344-350. [12]KEANE M T,SMYTH B.Good counterfactuals and where to find them:A case-based technique for generating counterfactuals for explainable ai(xai)[C]//International Conference on Case-Based Reasoning.Cham:Springer,2020:163-178. [13]GOYAL Y,WU Z,ERNST J,et al.Counterfactual visual explanations[C]//International Conference on Machine Learning.PMLR,2019:2376-2384. [14]LOOVEREN A V,KLAISE J.Interpretable counterfactual explanations guided by prototypes[C]//Joint European Confe-rence on Machine Learning and Knowledge Discovery in Databases.Cham:Springer,2021:650-665. [15]SMYTH B,KEANE M T.A Few Good Counterfactuals:Gene-rating Interpretable,Plausible and Diverse Counterfactual Explanations[J].arXiv:2101.09056,2021. [16]KARIMI A H,SCHÖLKOPF B,VALERA I.Algorithmicrecourse:from counterfactual explanations to interventions[C]//Proceedings of the 2021 ACM Conference on Fairness,Accountability,and Transparency.2021:353-362. [17]GRATH R M,COSTABELLO L,VAN C L,et al.Interpretable credit application predictions with counterfactual explanations[J].arXiv:1811.05245,2018. [18]RUSSELL C.Efficient search for diverse coherent explanations[C]//Proceedings of the Conference on Fairness,Accountabi-lity,and Transparency.2019:20-28. [19]MAHAJAN D,TAN C,SHARMA A.Preserving causal con-straints in counterfactual explanations for machine learning classifiers[J].arXiv:1912.03277,2019. [20]KARIMI A H,BARTHE G,BALLE B,et al.Model-agnostic counterfactual explanations for consequential decisions[C]//International Conference on Artificial Intelligence and Statistics.PMLR,2020:895-905. [21]KAUSHIK D,HOVY E,LIPTON Z C.Learning the difference that makes a difference with counterfactually-augmented data[J].arXiv:1909.12434,2019. [22]ZHAO W,OYAMA S,KURIHARA M.Generating naturalcounterfactual visual explanations[C]//Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence.2021:5204-5205. [23]GOMEZ O,HOLTER S,YUAN J,et al.Vice:Visual counterfactual explanations for machine learning models[C]//Procee-dings of the 25th International Conference on Intelligent User Interfaces.2020:531-535. [24]KOHAVI R,BECKER B.Adult [EB/OL].2019.(1996-05-01).http://archive.ics.uci.edu/ml/datasets/Adult. [25]HOFMANN H.Statlog(German Credit Data)[EB/OL].(1994-11-17).http://archive.ics.uci.edu/ml/datasets/statlog+(germag+credit+data). [26]ASUNCION A,NEWMAN D.UCI machine learning repository [EB/OL].[2013-05-28].http://archive.ics.uci.edu/ml. [27]MICCI-BARRECA D.A preprocessing scheme for high-cardi-nality categorical attributes in classification and prediction problems[J].ACM SIGKDD Explorations Newsletter,2001,3(1):27-32. [28]BIAU G,SCORNET E.A random forest guided tour[J].Test,2016,25(2):197-227. [29]SAFAVIAN S R,LANDGREBE D.A survey of decision tree classifier methodology[J].IEEE Transactions on Systems,Man,and Cybernetics,1991,21(3):660-674. [30]RISH I.An empirical study of the naive Bayes classifier[J].IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence,2001,3(22):41-46. [31]REFAEILZADEH P,TANG L,LIU H.Cross-validation[M].Encyclopedia of Database Systems,2009:532-538. |
[1] | ZHAO Lu, YUAN Li-ming, HAO Kun. Review of Multi-instance Learning Algorithms [J]. Computer Science, 2022, 49(6A): 93-99. |
[2] | CHENG Ke-yang, WANG Ning, CUI Hong-gang, ZHAN Yong-zhao. Interpretability Optimization Method Based on Mutual Transfer of Local Attention Map [J]. Computer Science, 2022, 49(5): 64-70. |
[3] | WEI Lin-jing, LIAN Zhi-chao, WANG Lian-guo and HOU Zhen-xing. Term and Semantic Difference Metric Based Document Clustering Algorithm [J]. Computer Science, 2016, 43(12): 229-233. |
|