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2004 JMLR JMLR 2004

Subgroup Discovery with CN2-SD

Abstract

This paper investigates how to adapt standard classification rule learning approaches to subgroup 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, CN2-SD , 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 CN2-SD 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 CN2-SD to a large traffic accident data set confirms these findings. [abs] [ pdf ][ ps.gz ][ ps ]

🧭 Keyword Pioneer — roc curve
🐝 Cross-Pollinator — Artificial Intelligence, Deep Learning, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing
🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
📈 Trend Setter — Data Mining

Authors

Nada Lavrač , Branko Kavšek , Peter Flach , Ljupčo Todorovski

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Unsupervised Learning Data Science & Analytics > Methods > Data Mining

Keywords

roc curve rule learning subgroup discovery classification rule cn2 algorithm rule evaluation
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