MDL-Based Decision Tree Pruning

How researchers see this paper

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

Building Decision Trees with Constraints (2003, methodology): “An important class of pruning algorithms are those based on the Minimum Description Length (MDL) principle (Fayyad and Irani, 1993; Mehta et al., 1995; Quinlan and Rivest, 1989; Wallace and Patrick, 1993).”
SLIQ: A Fast Scalable Classifier for Data Mining (1996, methodology): “However, the pruning algorithm in [6] was limited; it either pruned all or none of the children of a node in the decision tree.”
DATA MINING TECHNIQUES (2003, background): “More details on tree pruning can be found in [55, 56, 65, 67]; we will focus on the build tree step.”
Top-down induction of decision trees classifiers - a survey (2005, methodology): “[44] used the Minimum Description Length for evaluating the generalized accuracy of a node.”
An Optimal Constrained Pruning Strategy for Decision Trees (2009, methodology): “Published online in Articles in Advance August 8, 2008.”
Subtree Replacement in Decision Tree Simplification (2012, methodology): “Finally, our approach can be readily applied to integrate grafting with other old and new bottom-up pruning algorithms, such as CART cost complexity [5], minimum description length [18], cost sensitive [25], and k-norm [28] pruning.”