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Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers
Högskolan i Borås, Institutionen Handels- och IT-högskolan. (CSL@BS)
Högskolan i Borås, Institutionen Handels- och IT-högskolan. (CSL@BS)
Högskolan i Borås, Institutionen Handels- och IT-högskolan. (CSL@BS)
Högskolan i Borås, Institutionen Handels- och IT-högskolan. (CSL@BS)
2014 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

In the conformal prediction literature, it appears axiomatic that transductive conformal classifiers possess a higher predictive efficiency than inductive conformal classifiers, however, this depends on whether or not the nonconformity function tends to overfit misclassified test examples. With the conformal prediction framework’s increasing popularity, it thus becomes necessary to clarify the settings in which this claim holds true. In this paper, the efficiency of transductive conformal classifiers based on decision tree, random forest and support vector machine classification models is compared to the efficiency of corresponding inductive conformal classifiers. The results show that the efficiency of conformal classifiers based on standard decision trees or random forests is substantially improved when used in the inductive mode, while conformal classifiers based on support vector machines are more efficient in the transductive mode. In addition, an analysis is presented that discusses the effects of calibration set size on inductive conformal classifier efficiency.

sted, utgiver, år, opplag, sider
Springer , 2014.
Serie
IFIP Advances in Information and Communication Technology, ISSN 1868-4238 ; 437
Emneord [en]
Conformal Prediction, Machine learning, Data mining
HSV kategori
Identifikatorer
URN: urn:nbn:se:hb:diva-7323DOI: 10.1007/978-3-662-44722-2_28Lokal ID: 2320/14626ISBN: 978-3-662-44721-5 (tryckt)ISBN: 978-3-662-44722-2 (tryckt)OAI: oai:DiVA.org:hb-7323DiVA, id: diva2:888036
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: 2015-12-22 Laget: 2015-12-22 Sist oppdatert: 2018-01-10

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