ML Anthology

Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms

Marina M.-C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Gunnar Rätsch, Marius Kloft
ECML-PKDD 2015 pp. 137-153
doi:10.1007/978-3-319-23525-7_9 /ecmlpkdd/2015/vidovic2015ecmlpkdd-opening/

Abstract

This work is in the context of kernel-based learning algorithms for sequence data. We present a probabilistic approach to automatically extract, from the output of such string-kernel-based learning algorithms, the subsequences—or motifs —truly underlying the machine’s predictions. The proposed framework views motifs as free parameters in a probabilistic model, which is solved through a global optimization approach. In contrast to prevalent approaches, the proposed method can discover even difficult, long motifs, and could be combined with any kernel-based learning algorithm that is based on an adequate sequence kernel. We show that, by using a discriminate kernel machine such as a support vector machine, the approach can reveal discriminative motifs underlying the kernel predictor. We demonstrate the efficacy of our approach through a series of experiments on synthetic and real data, including problems from handwritten digit recognition and a large-scale human splice site data set from the domain of computational biology.

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Cite

Text

Vidovic et al. "Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015. doi:10.1007/978-3-319-23525-7_9

Markdown

[Vidovic et al. "Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2015.](https://mlanthology.org/ecmlpkdd/2015/vidovic2015ecmlpkdd-opening/) doi:10.1007/978-3-319-23525-7_9

BibTeX

@inproceedings{vidovic2015ecmlpkdd-opening,
  title     = {{Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms}},
  author    = {Vidovic, Marina M.-C. and Görnitz, Nico and Müller, Klaus-Robert and Rätsch, Gunnar and Kloft, Marius},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2015},
  pages     = {137-153},
  doi       = {10.1007/978-3-319-23525-7_9},
  url       = {https://mlanthology.org/ecmlpkdd/2015/vidovic2015ecmlpkdd-opening/}
}