Here is a short list of incremental decision tree methods, organized by their (usually non-incremental) parent algorithms.
CART[1] (1984) is a nonincremental decision tree inducer for both classification and regression problems.
CART traces its roots to AID (1963)[2] ID3 (1986)[4] and C4.5 (1993)[5] were developed by Quinlan and have roots in Hunt's Concept Learning System (CLS, 1966)[6] The ID3 family of tree inducers was developed in the engineering and computer science communities.
[7] Notable among these was Schlimmer and Granger's STAGGER (1986),[13] which learned disjunctive concepts incrementally.
Experience with these earlier systems and others, to include incremental tree-structured unsupervised learning, contributed to a conceptual framework for evaluating incremental decision tree learners specifically, and incremental concept learning generally, along four dimensions that reflect the inherent tradeoffs between learning cost and quality:[7] (1) cost of knowledge base update, (2) the number of observations that are required to converge on a knowledge base with given characteristics, (3) the total effort (as a function of the first two dimensions) that a system exerts, and the (4) quality (often consistency) of the final knowledge base.