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.