Global Induction of Oblique Decision Trees: An Evolutionary Approach

By Marek Kretowski, Marek Grzes. 2005

23 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

How researchers see this paper

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

In Artificial Intelligence

1. Yashesh D. Dhebar
2. Subramanya P. Nageshrao
3. André Carlos Ponce de Leon Ferreira de Carvalho
4. Rodrigo C. Barros
5. Márcio Porto Basgalupp
6. Kalyanmoy Deb
7. Alex Alves Freitas
8. Dimitar Filev

In Computational Theory and Mathematics

1. Yashesh D. Dhebar
2. André Carlos Ponce de Leon Ferreira de Carvalho
3. Rodrigo C. Barros
4. Márcio Porto Basgalupp
5. Kalyanmoy Deb
6. Alex Alves Freitas
7. Dimitar Filev
8. Ling Zhu

In Information Systems

1. André Carlos Ponce de Leon Ferreira de Carvalho
2. Rodrigo C. Barros
3. Márcio Porto Basgalupp
4. Kalyanmoy Deb
5. Alex Alves Freitas
6. Yashesh D. Dhebar
7. Cynthia Rudin
8. Been Kim

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].”
• A Survey of Evolutionary Algorithms for Decision-Tree Induction (2012, background): “Kretowski and Grzes [69]–[71] also implemented this approach but instead of using a well-established split measure such as the gain ratio or the gini index, they opted for a dipolar split measure.”
• 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.”