Hybrid decision tree

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Transductive Multi-View Zero-Shot Learning (2015, background): “Additionally, zero-shot learning can be taken as the generalisation of class-incremental learning (C-IL) [4, 35] or life-long learning [26].”
Learning with Augmented Class by Exploiting Unlabeled Data (2014, methodology): “A branch of methods dealing with the changing environments is the incremental learning, which mainly includes sub-branches of the example-incremental learning (E-IL) (Ruping 2001; Polikar et al. 2001; Fern and Givan 2003), the attribute-incremental learning (A-IL) (Vapnik, Vashist, and Pavlovitch 20”
Label Mapping Neural Networks with Response Consolidation for Class Incremental Learning (2019, background): “We expect the model should be updated continually so that the knowledge embedded in new classes can be incorporated without sacriﬁcing the learned knowledge of old classes too much [19].”
Machine learning: a review of classification and combining techniques (2006, methodology): “Zhou and Chen (2002) generated a binary hybrid decision tree according to the binary information gain ratio criterion.”
Weighted Oblique Decision Trees (2019, methodology): “…to simplicity and interpretability, a large number of axis-parallel decision trees have been developed in the literature (Domingos and Hulten 2000; Zhou and Chen 2002; Geurts, Ernst, and Wehenkel 2006; Shotton et al. 2013a; Abuzaid et al. 2016), and decision trees have been used as base learners…”