Lazy Decision Trees

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance (2016, methodology): “For the latter, we employ an algorithm similar in spirit to lazy decision trees [4], where we construct c greedily.”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “This methodology consists in subdividing the original problem into smaller ones, such as the procedure used in decision trees, and can lead to data fragmentation [49], that is, to obtain several partitions of data with a few representation of instances.”
Top 10 algorithms in data mining (2007, background): “[26] noted that 50% of the CART code was devoted to missing value handling; it is thus unlikely that Quinlan’s experimental version properly replicated the entire CART surrogate mechanism.”
Handling Missing Values when Applying Classification Models (2007, methodology): “This can be accomplished by delaying model induction until a prediction is required, a strategy presented as " lazy " classificationtree induction by Friedman et al. (1996).”
Mining with rarity: a unifying framework (2004, background): “This process may lead to data fragmentation [20].”