Discovering Useful Compact Sets of Sequential Rules in a Long Sequence
Machine Learning
2023-01-02 v2 Artificial Intelligence
Abstract
We are interested in understanding the underlying generation process for long sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine small and meaningful sets of sequential rules. The rules are selected using an MDL-inspired criterion that favors compactness and relies on a novel rule-based encoding scheme for sequences. Our evaluation shows that COSSU can successfully retrieve relevant sets of closed sequential rules from a long sequence. Such rules constitute an interpretable model that exhibits competitive accuracy for the tasks of next-element prediction and classification.
Cite
@article{arxiv.2109.07519,
title = {Discovering Useful Compact Sets of Sequential Rules in a Long Sequence},
author = {Erwan Bourrand and Luis Galárraga and Esther Galbrun and Elisa Fromont and Alexandre Termier},
journal= {arXiv preprint arXiv:2109.07519},
year = {2023}
}
Comments
8 pages, published in the proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence