The Decision List Machine
By Marina Sokolova, Mario Marchand, Nathalie Japkowicz, John Shawe-Taylor. 2002
26 citations recorded by the source corpus.
Subfields: Evaluation & Benchmarks, Statistical Learning Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Evaluation & Benchmarks
- Cynthia Rudin
- Lesia Semenova
- Zhi Chen
- Mario Marchand
- Marina Sokolova
- Chudi Zhong
- Haiyang Huang
- Allison Chang
In Statistical Learning Theory
- Cynthia Rudin
- Margo I. Seltzer
- Chudi Zhong
- Lesia Semenova
- Zhi Chen
- Haiyang Huang
- Allison Chang
- Mario Marchand
Citations in the corpus
- Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, methodology): “…is scored separately according to a scoring function (Rivest, 1987; Clark and Niblett, 1989; Liu et al., 1998; Li et al., 2001; Yin and Han, 2003; Sokolova et al., 2003; Marchand and Sokolova, 2005; Vanhoof and Depaire, 2010; Rudin et al., 2013; Clark and Boswell, 1991; Gaines and Compton,…”
- Learning Certifiably Optimal Rule Lists (2017, methodology): “Methods for decision list learning construct rule lists iteratively in a greedy way (Rivest, 1987; Sokolova et al., 2003; Marchand and Sokolova, 2005; Rudin et al., 2013; Goessling and Kang, 2015); these too have no guarantee of optimality, and tend not to produce optimal rule lists in general.”
- ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification (2012, background): “The second category of sorted-rule-based classification algorithms produces decision lists [see Rivest, 1987, Klivans and Servedio, 2006, Sokolova et al., 2003, Anthony, 2005, Long and Servedio, 2007, Marchand and Sokolova, 2005, Rudin et al., 2011].”
- Learning with Decision Lists of Data-Dependent Features (2005, methodology): “In this paper, we use the set of features known as data-dependent balls (Marchand and Shawe-Taylor, 2001; Sokolova et al., 2003) and the set of features known as data-depen nt half-spaces (Marchand et al., 2003).”