ENDER: a statistical framework for boosting decision rules

By Krzysztof Dembczynski, W. Kotłowski, R. Słowiński. 2010

86 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

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. Gérard Biau
2. Erwan Scornet
3. Sanjeeb Dash
4. Sébastien Da Veiga
5. Clément Bénard
6. Dmitry M. Malioutov
7. Kuldeep S. Meel
8. Marcel A. J. van Gerven

In Computational Theory and Mathematics

1. Sébastien Da Veiga
2. Clément Bénard
3. Dmitry M. Malioutov
4. Sanjeeb Dash
5. Kush R. Varshney
6. Gérard Biau
7. Hugo Jair Escalante
8. Isabelle M Guyon

Citations in the corpus

• MLIC: A MaxSAT-Based Framework for Learning Interpretable Classification Rules (2018, methodology): “Greedy approaches are used with a principled objective function in ENDER [17] and Set covering machines [25].”
• Boolean Decision Rules via Column Generation (2018, neutral): “Rule set models have been generalized to rule ensembles [17, 29, 20], using boosting and linear combination rather than logical disjunction; the interpretability of such models is again not comparable to rule sets.”
• Interpretable Random Forests via Rule Extraction (2020, background): “A large number of rule algorithms have been developed, among which the most influential Decision List (Rivest, 1987), CN2 (Clark & Niblett, 1989), C4.5 (Quinlan, 1992), IREP (Incremental Reduced Error Pruning, F¨urnkranz & Widmer, 1994), RIPPER (Repeated Incremental Pruning to Produce Error Reduction”
• Explainable and Interpretable Models in Computer Vision and Machine Learning (2017, methodology): “1Some algorithms, such as ENDER (Dembczyński et al. 2010), also find rules that directly minimize a regularized loss function.”
• Screening for learning classification rules via Boolean compressed sensing (2014, background): “As an example in a management setting, a rule-based classifier for predicting the voluntary resignation of salespeople is [7]: • Job Role = Specialty Software Sales Rep; AND • Base Salary ≤ 75,168; AND • Months Since Promoted > 13; AND • Months Since Promoted ≤ 30; AND • Quota-Based Compensation =…”
• pre: An R Package for Fitting Prediction Rule Ensembles (2017, methodology): “Notably, Dembczy´nski et al. (2010) report that predictive accuracy is hardly affected by the choice of loss function, but substantially affected by the regularization methods employed.”
• Exact Rule Learning via Boolean Compressed Sensing (2013, methodology): “ENDER uses empirical risk to both learn rules and ensembles, but learns rules in a greedy manner (Dembczyński et al., 2010).”
• SIRUS: making random forests interpretable (2019, methodology): “The last decade has seen a resurgence of rule models, especially with RuleFit (Friedman et al., 2008), Node harvest (Meinshausen, 2010), ENDER (Ensemble of Decision Rules, Dembczyński et al., 2010), and BRL (Bayesian Rule Lists, Letham et al., 2015).”