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A simple, fast, and effective rule learner
 IN PROCEEDINGS OF ANNUAL CONFERENCE OFAMERICAN ASSOCIATION FOR ARTI CIAL INTELLIGENCE
, 1999
"... We describe SLIPPER, a new rule learner that generates rulesets by repeatedly boosting a simple, greedy, rulebuilder. Like the rulesets built by other rule learners, the ensemble of rules created by SLIPPER is compact and comprehensible. This is made possible by imposing appropriate constraints on ..."
Abstract

Cited by 99 (3 self)
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We describe SLIPPER, a new rule learner that generates rulesets by repeatedly boosting a simple, greedy, rulebuilder. Like the rulesets built by other rule learners, the ensemble of rules created by SLIPPER is compact and comprehensible. This is made possible by imposing appropriate constraints on the rulebuilder, and by use of a recentlyproposed generalization of Adaboost called confidencerated boosting. In spite of its relative simplicity, SLIPPER is highly scalable, and an effiective learner. Experimentally, SLIPPER scales no worse than O(n log n), where n is the number of examples, and on a set of 32 benchmark problems, SLIPPER achieves lower error rates than RIPPER 20 times, and lower error rates than C4.5rules 22 times.
Subgroup Discovery with CN2SD
 Journal of Machine Learning Research
, 2004
"... discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2SD, developed by modifying parts of the CN2 classification rule learner: its covering algorit ..."
Abstract

Cited by 62 (10 self)
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discovery. The goal of subgroup discovery is to find rules describing subsets of the population that are sufficiently large and statistically unusual. The paper presents a subgroup discovery algorithm, CN2SD, developed by modifying parts of the CN2 classification rule learner: its covering algorithm, search heuristic, probabilistic classification of instances, and evaluation measures. Experimental evaluation of CN2SD on 23 UCI data sets shows substantial reduction of the number of induced rules, increased rule coverage and rule significance, as well as slight improvements in terms of the area under ROC curve, when compared with the CN2 algorithm. Application of CN2SD to a large traffic accident data set confirms these findings.
ExpertGuided Subgroup Discovery: Methodology and Application
 Journal of Artificial Intelligence Research
, 2002
"... This paper presents an approach to expertguided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The othe ..."
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Cited by 40 (8 self)
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This paper presents an approach to expertguided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.
Active subgroup mining: a case study in coronary
, 2003
"... heart disease risk group detection ..."
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