Rule learning with monotonicity constraints

By W. Kotłowski, R. Słowiński. 2009

56 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. Andrew Cotter
2. Maya R. Gupta
3. Wojtek Moczydlowski
4. Alexander Mangylov
5. Alexander Van Esbroeck
6. Mahdi Milani Fard
7. Kevin Robert Canini
8. Konstantin Voevodski

In Computational Theory and Mathematics

1. Andrew Cotter
2. Maya R. Gupta
3. Kevin Robert Canini
4. Ohad Shamir

In Information Systems

1. Mahdi Milani Fard

Citations in the corpus

• Monotonic Calibrated Interpolated Look-Up Tables (2015, background): “ed version of k-NN, they can enforce monotonic outputs. Similar pre-processing of samples can be used to encourage any subsequently trained classier to be more monotonic (Feelders, 2010). Similarly, Kotlowski and Slowinski (2009) try to solve the isotonic regression problem to re-label the dataset t”
• Fast and Flexible Monotonic Functions with Ensembles of Lattices (2016, background): “Another approach that uses an ensemble of rules on a modified training set, scales poorly with the dataset size and does not provide monotonicity guarantees on test samples [7].”