2014 BoxDrawingsforLearningwithImbal

• (Goh & Rudin, 2014) ⇒ Siong Thye Goh, and Cynthia Rudin. (2014). “Box Drawings for Learning with Imbalanced Data.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623648

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Box+Drawings+for+Learning+with+Imbalanced+Data
• http://dl.acm.org/citation.cfm?id=2623330.2623648&preflayout=flat#citedby

Quotes

Author Keywords

• Classification; decision trees; imbalanced data; learning

Abstract

The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers are disjunctions of conjunctions, and are created as unions of parallel axis rectangles around the positive examples, and thus have the benefit of being interpretable. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. Regularization is introduced to improve generalization performance. The second method uses an approximation in order to assist with scalability. Specifically, it follows a characterize then discriminate approach, where the positive class is characterized first by boxes, and then each box boundary becomes a separate discriminative classifier. This method has the computational advantages that it can be easily parallelized, and considers only the relevant regions of feature space.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 BoxDrawingsforLearningwithImbal	Siong Thye Goh Cynthia Rudin			Box Drawings for Learning with Imbalanced Data				10.1145/2623330.2623648		2014