AI RESEARCH
MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound
arXiv CS.LG
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ArXi:2605.11617v1 Announce Type: new Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decision boundaries. Despite these, they still fail in online class-incremental learning due to two coupled miscalibrations: (i) their split criterion grows unreliable as the class count K expands, and (ii) the absence of knowledge transfer at split time. Both failures share a common root: the range of Information Gain intrinsically scales with log2 K.