Using Qualitative Models to Guide Inductive Learning

How researchers see this paper

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

Integrating Classification and Association Rule Mining (1998, background): “The framework helps to solve the understandability problem ( Clark and Matwin 1993; Pazzani, Mani and Shankle 1997) in classification rule mining.”
Learning from Sparse Data by Exploiting Monotonicity Constraints (2005, background): “We show that this yields improved accuracy, particularly with very small training sets (e.g. less than 10 examples).”
Acceptance of Rules Generated by Machine Learning among Medical Experts (2001, methodology): “The most similar work to the research reported in this paper is Clark & Matwins’ (20) extensions to CN2 with qualitative models.”
Improving SVM accuracy by training on auxiliary data sources (2004, background): “These e orts include learning by exploiting background knowledge (e.g., Clark & Matwin, 1993) and Appearing in Proceedings of the 21 st International Conference on Machine Learning, Ban , Canada, 2004.”
Post-Analysis of Learned Rules (1996, background): “Most of the work on machine learning focuses on the generation of rules from various types of datasets as well as pruning of the generated rules (e.g., Quinlan 1992; Breiman et al 1984; Clark & Matwin 1993).”
Attribute reduction in decision-theoretic rough set models (2008, background): “Many quantitative measures associated with rules have been studied [3,5,46,49].”