About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
STRL 2022
Conference paper
Learning binary classification rules for sequential data
Abstract
Discovering patterns for classification of sequential data is of key importance for a variety of fields, ranging from genomics to fraud detection. In this short paper, we propose a differentiable method to discover both local and global patterns for rule-based binary classification. Key to this end-to-end differentiable approach is that the patterns used in the rules are learned alongside the rules themselves.