Box drawings for learning with imbalanced data

Published

Conference Paper

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 \textit{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. © 2014 ACM.

Full Text

Duke Authors

Cited Authors

  • Goh, ST; Rudin, C

Published Date

  • January 1, 2014

Published In

  • Proceedings of the Acm Sigkdd International Conference on Knowledge Discovery and Data Mining

Start / End Page

  • 333 - 342

International Standard Book Number 13 (ISBN-13)

  • 9781450329569

Digital Object Identifier (DOI)

  • 10.1145/2623330.2623648

Citation Source

  • Scopus