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Learning Decision Forest from Evidential Data: the Random Training Set Sampling Approach
Univ Jinan, CHI.
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier. Blekinge Inst Technol, Dept Creat Technol, Karlskrona, Sweden..ORCID-id: 0000-0001-5824-425X
Univ Jinan, CHI.
2017 (engelsk)Inngår i: International Conference on Systems and Informatics, IEEE , 2017, s. 1423-1428Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

To learn decision trees from uncertain data modelled by mass functions, the random training set sampling approach for learning belief decision forests is proposed. Given an uncertain training set, a collection of simple belief decision trees are trained separately on each corresponding new set drawn by random sampling from the original one. Then the final prediction is made by majority voting. After discussing the selection of parameters for belief decision forests, experiments on Balance scale data are carried on for performance validation. Results show that with different kinds of uncertainty, the proposed method guarantees an obvious improvement in classification accuracy.

sted, utgiver, år, opplag, sider
IEEE , 2017. s. 1423-1428
Serie
International Conference on Systems and Informatics, ISSN 2474-0217
Emneord [en]
Decision trees, Decision Forest, Learning algorithms
HSV kategori
Identifikatorer
URN: urn:nbn:se:bth-16125ISI: 000427752100257ISBN: 978-1-5386-1107-4 (tryckt)OAI: oai:DiVA.org:bth-16125DiVA, id: diva2:1201513
Konferanse
4th International Conference on Systems and Informatics (ICSAI), Hangzhou,
Tilgjengelig fra: 2018-04-26 Laget: 2018-04-26 Sist oppdatert: 2018-05-24bibliografisk kontrollert

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