Pruning for Monotone Classification Trees

By Ad Feelders, Martijn Pardoel. 2003

47 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. Cynthia Rudin
2. Alex Alves Freitas
3. Bart Baesens
4. David Martens
5. Dolores Romero Morales
6. Emilio Carrizosa
7. C. Mues
8. Marco F. Huber

In Computational Theory and Mathematics

1. Cynthia Rudin
2. Alex Alves Freitas
3. Bart Baesens
4. David Martens
5. Jan Vanthienen
6. Elen Lima
7. Rudy Setiono
8. Ad Feelders

In Information Systems

1. Cynthia Rudin
2. Alex Alves Freitas
3. David Martens
4. Bart Baesens
5. C. Mues
6. Jan Vanthienen
7. Elen Lima
8. Rudy Setiono

Citations in the corpus

• An optimization approach to learning falling rule lists (2017, background): “There are some works on decision trees with monotonicity constraints (e.g. Altendorf et al., 2005; Ben-David, 1995; Feelders and Pardoel, 2003), but they focus mostly on enforcing the monotonic relationship between certain attributes and ordinal class labels.”
• Building Acceptable Classification Models (2010, background): “Another manner to achieve monotone classification models in a postprocessing step is by pruning classification trees [21].”
• Comprehensible classification models: a position paper (2014, methodology): “Hence, in [22] a standard (not necessarily monotone) tree is first built, and then the tree is pruned with a method that guarantees that the pruned tree is monotone.”
• Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, methodology): “More formally (similarly to [24]), given a data set D = {x, y}i=1, with x = (x1, x2, .”
• Nearest Neighbour Classification with Monotonicity Constraints (2008, background): “Examples are: classification trees [7,12,22], neural networks [3,26], and Bayesian networks [1,13].”
• Domain knowledge integration in data mining using decision tables: case studies in churn prediction (2009, background): “One of the main aspects related to domain knowledge integration that is not explicitly incorporated by the three previous studies is the monotonicity constraint (Ben-David, 1995; Sill, 1998; Feelders and Pardoel, 2003; Velikova and Daniels, 2004; Altendorf et al, 2005; Martens et al, 2006; Van Geste”