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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015
EditorsVitor Santos Costa, Carlos Soares, Annalisa Appice, Annalisa Appice, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, João Gama, Alípio Jorge, Pedro Pereira Rodrigues, João Gama, Vitor Santos Costa, Alípio Jorge, Annalisa Appice, Pedro Pereira Rodrigues, João Gama, Annalisa Appice, Carlos Soares, Alípio Jorge, João Gama, Pedro Pereira Rodrigues, Vitor Santos Costa, Carlos Soares, Alípio Jorge
PublisherSpringer Verlag
Pages137-153
Number of pages17
ISBN (Print)9783319235240, 9783319235240, 9783319235240, 9783319235240
DOIs
Publication statusPublished - 2015
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015 - Porto, Portugal
Duration: 2015 Sep 72015 Sep 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9285
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2015
CountryPortugal
CityPorto
Period15/9/715/9/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Opening the black box: Revealing interpretable sequence motifs in kernel-based learning algorithms'. Together they form a unique fingerprint.

  • Cite this

    Vidovic, M. M. C., Görnitz, N., Müller, K. R., Rätsch, G., & Kloft, M. (2015). Opening the black box: Revealing interpretable sequence motifs in kernel-based learning algorithms. In V. S. Costa, C. Soares, A. Appice, A. Appice, P. P. Rodrigues, V. S. Costa, C. Soares, J. Gama, A. Jorge, P. P. Rodrigues, J. Gama, V. S. Costa, A. Jorge, A. Appice, P. P. Rodrigues, J. Gama, A. Appice, C. Soares, A. Jorge, J. Gama, P. P. Rodrigues, V. S. Costa, C. Soares, ... A. Jorge (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015 (pp. 137-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9285). Springer Verlag. https://doi.org/10.1007/978-3-319-23525-7_9