Rule Extraction with Guaranteed FidelityShow others and affiliations
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]
This paper extends the conformal prediction framework to
rule extraction, making it possible to extract interpretable models from
opaque models in a setting where either the infidelity or the error rate
is bounded by a predefined significance level. Experimental results on 27
publicly available data sets show that all three setups evaluated produced
valid and rather efficient conformal predictors. The implication is that
augmenting rule extraction with conformal prediction allows extraction
of models where test set errors or test sets infidelities are guaranteed
to be lower than a chosen acceptable level. Clearly this is beneficial for
both typical rule extraction scenarios, i.e., either when the purpose is
to explain an existing opaque model, or when it is to build a predictive
model that must be interpretable.
Place, publisher, year, edition, pages
Springer , 2014.
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 437
Keywords [en]
Rule extraction, Conformal Prediction, Decision trees, Machine learning, Data mining
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:hb:diva-7322DOI: 10.1007/978-3-662-44722-2_30Local ID: 2320/14625ISBN: 978-3-662-44721-5 (print)ISBN: 978-3-662-44722-2 (print)OAI: oai:DiVA.org:hb-7322DiVA, id: diva2:888035
Conference
Artificial Intelligence Applications and Innovations
Note
Sponsorship:
This work was supported by the Swedish Foundation for Strategic Research through
the project High-Performance Data Mining for Drug Effect Detection (IIS11-0053)
and the Knowledge Foundation through the project Big Data Analytics by Online
Ensemble Learning (20120192).
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