Adaptive Decision Forest: An incremental machine learning framework
作者：
Highlights：
• An Incremental Machine Learning Framework.
• Justification of the basic concepts and theoretical insights of the technique.
• Two novel theorems, some empirical analyses and a complexity analysis of all techniques.
• Experimentation on ten data sets, two evaluation criteria, two statistical analyses.
• Comparison with eight existing techniques.
摘要
•An Incremental Machine Learning Framework.•Justification of the basic concepts and theoretical insights of the technique.•Two novel theorems, some empirical analyses and a complexity analysis of all techniques.•Experimentation on ten data sets, two evaluation criteria, two statistical analyses.•Comparison with eight existing techniques.
论文关键词：Incremental learning,Decision forest algorithm,Concept drift,Big data,Online learning
论文评审过程：Received 29 August 2020, Revised 4 May 2021, Accepted 20 September 2021, Available online 22 September 2021, Version of Record 8 October 2021.
论文官网地址：https://doi.org/10.1016/j.patcog.2021.108345
