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Rule Extraction with Guaranteed Fidelity
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID-id: 0000-0003-0412-6199
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID-id: 0000-0003-0274-9026
Vise andre og tillknytning
2014 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer, 2014.
Serie
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 437
Emneord [en]
Rule extraction, Conformal Prediction, Decision trees, Machine learning, Data mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-38087DOI: 10.1007/978-3-662-44722-2_30ISBN: 978-3-662-44721-5 (tryckt)ISBN: 978-3-662-44722-2 (tryckt)OAI: oai:DiVA.org:hj-38087DiVA, id: diva2:1163341
Konferanse
Artificial Intelligence Applications and Innovations
Merknad

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).

Tilgjengelig fra: 2017-12-06 Laget: 2017-12-06 Sist oppdatert: 2018-01-13bibliografisk kontrollert

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Johansson, UlfLöfström, Tuve

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