Induction of Multivariate Decision Trees by Using Dipolar Criteria

By Leon Bobrowski, Marek Kretowski. 2000

25 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.

1. Yashesh D. Dhebar
2. Subramanya P. Nageshrao
3. Ling Zhu
4. Dimitar Filev
5. Kalyanmoy Deb
6. Yaochu Jin
7. Ye Tian
8. Ran Cheng

Citations in the corpus

• Toward Interpretable-AI Policies Using Evolutionary Nonlinear Decision Trees for Discrete-Action Systems (2020, methodology): “The initial point required for SQP is obtained using the mixed dipole concept [30], [36]–[38].”
• An Evolutionary Algorithm for Oblique Decision Tree Induction (2004, methodology): “[2] is based on the dipolar criterion functions and exploits the basis exchange algorithm as an optimization procedure.”
• 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.”