Interpret When Possible:A Tree-Based Hybrid Framework for Interpretable ClassificationOA
Interpret When Possible:A Tree-Based Hybrid Framework for Interpretable Classification
Yifan Li;Shuhan Qi;Lei Cui;Chao Xing;Lei Zhang;Xuan Wang
School of Computer Science and Technology,Harbin Institute of Technology,Shenzhen 518000,China||Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies,Shenzhen 518000,ChinaSchool of Computer Science and Technology,Harbin Institute of Technology,Shenzhen 518000,China||Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies,Shenzhen 518000,ChinaSchool of Computer Science and Technology,Harbin Institute of Technology,Shenzhen 518000,China||Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies,Shenzhen 518000,ChinaShenzhen Zhice Technology Co.,Ltd.,Shenzhen 518000,ChinaPengcheng Laboratory,Shenzhen 518000,ChinaSchool of Computer Science and Technology,Harbin Institute of Technology,Shenzhen 518000,China||Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies,Shenzhen 518000,China
interpretable machine learningDecision Trees(DTs)classification
interpretable machine learningDecision Trees(DTs)classification
《大数据挖掘与分析（英文版）》 2026 (1)
263-283,21
This research was funded by the Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies(No.2022B1212010005),the National Natural Science Foundation of China(No.62376073),the Natural Science Foundation of Guangdong(No.2024A1515030024),and the Colleges and Universities Stable Support Project of Shenzhen(No.GXWD20220811173149002).
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