Abstract
FACT, the First G-APD Cherenkov Telescope, detects air showers induced by high-energetic cosmic particles. It is desirable to classify a shower as being induced by a gamma ray or a background particle. Generally, it is nontrivial to get any feedback on the real-life training task, but we can attempt to understand how our classifier works by investigating its performance on Monte Carlo simulated data. To this end, in this paper we present the SCaPE (Soft Classifier Performance Evaluation) model class for Exceptional Model Mining, which is a Local Pattern Mining framework devoted to highlighting unusual interplay between multiple targets. The SCaPE model class highlights subspaces of the search space where the classifier performs particularly well or poorly. These subspaces arrive in terms of conditions on attributes of the data, hence they come in a language a human understands, which should help us understand where our classifier does (not) work.
This Nectar Track submission presents the paper [4]. A significantly longer version of that paper appeared as a technical report [5].
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Anderhub, H., Backes, M., Biland, A., et al.: Design and Operation of FACT - the First G-APD Cherenkov Telescope, arXiv:1304.1710 (astro-ph.IM)
Bretz, T., Anderhub, H., et al.: FACT – The First G-APD Cherenkov Telescope: Status and Results, arXiv:1308.1512 (astro-ph.IM)
Duivesteijn, W.: Exceptional Model Mining, Ph.D. thesis, Leiden University (2013)
Duivesteijn, W., Thaele, J.: Understanding where your classifier does (Not) work – the SCaPE model class for EMM. In: Proc. ICDM, pp. 809–814 (2014)
Duivesteijn, W., Thaele, J.: Understanding Where Your Classifier Does (Not) Work – the SCaPE Model Class for Exceptional Model Mining, technical report 09/2014 of SFB876 at TU Dortmund (2014)
Henelius, A., Puolamäki, K., Boström, H., Asker, L., Papapetrou, P.: A peek into the black box: exploring classifiers by randomization. Data Mining and Knowledge Discovery 28(5–6), 1503–1529 (2014)
Leman, D., Feelders, A., Knobbe, A.J.: Exceptional model mining. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 1–16. Springer, Heidelberg (2008)
Morik, K., Boulicaut, J.F., Siebes, A. (eds.): Local Pattern Detection. Springer, New York (2005)
Tsoumakas, G., Katakis, I., Vlahavas, I.P.: Mining multi-label data. In: Data Mining and Knowledge Discovery Handbook, pp. 667–685. Springer (2010)
Vanschoren, J., Blockeel, H.: Towards understanding learning behavior. In: Proc. BENELEARN, pp. 89–96 (2006)
Vilalta, R., Drissi, Y.: A Perspective View and Survey of Meta-Learning. Artificial Intelligence Review 18(2), 77–95 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Duivesteijn, W., Thaele, J. (2015). Understanding Where Your Classifier Does (Not) Work. In: Bifet, A., et al. Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2015. Lecture Notes in Computer Science(), vol 9286. Springer, Cham. https://doi.org/10.1007/978-3-319-23461-8_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-23461-8_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23460-1
Online ISBN: 978-3-319-23461-8
eBook Packages: Computer ScienceComputer Science (R0)