Learning decision trees for loss minimization in multi-class problems

By Dragos D. Margineantu, Thomas G. Dietterich. 1999

How researchers see this paper

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

1. Faisal Kamiran
2. Indrė Žliobaitė
3. Toon Calders
4. Lu Zhang
5. Sara Hajian
6. Asmita Kashid
7. Muhammad Bilal Zafar
8. Luca Oneto

Citations in the corpus

• Three naive Bayes approaches for discrimination-free classification (2010, background): “In Cost-Sensitive and Utility-Bases learning [3, 5, 9], it is assumed that not all types of prediction errors are equal and not all examples are as important.”
• Discrimination-aware classification (2011, background): “In CostSensitive and Utility-Bases learning [21, 33, 59], it is assumed that not all types of”
• Discrimination Aware Decision Tree Learning (2010, background): “In Cost-Sensitive and Utility-Based learning [8, 12], it is assumed that not all types of prediction errors are equal and not all examples are as important.”