An Evolutionary Algorithm for Oblique Decision Tree Induction

48 citations recorded by the source corpus.

How researchers see this paper

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

Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, background): “3) to ﬁnd closed-loop NLDT*. Table VI shows that for the pruned NLDT-3, which comprises of the top three layers and involves only four rules of original 26-rule NLDT OL (i.e., NLDT-6), the closed-loop performance increases from 51% to 96% (NLDT*-3 results in Table VI) after reoptimizing its weights…”
A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “Kretowsky [119] proposes an EA that evolves hyperplanes for oblique decision trees.”
Interpretable Rule Discovery Through Bilevel Optimization of Split-Rules of Nonlinear Decision Trees for Classification Problems (2020, methodology): “Instead of creating every population member randomly, we use the mixed dipole concept [38], [9], [39] to facilitate faster convergence towards optimal (or near-optimal) values of w and Θ.”