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Consensus decision making in random forests
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.ORCID iD: 0000-0002-9316-4842
2015 (English)In: Revised Selected Papers of the First International Workshop on Machine Learning, Optimization, and Big Data, 2015, Vol. 9432, 347-358 p.Conference paper (Refereed)
Abstract [en]

The applications of Random Forests, an ensemble learner, are investigated in different domains including malware classification. Random Forests uses the majority rule for the outcome, however, a decision from the majority rule faces different challenges such as the decision may not be representative or supported by all trees in Random Forests. To address such problems and increase accuracy in decisions, a consensus decision making (CDM) is suggested. The decision mechanism of Random Forests is replaced with the CDM. The updated Random Forests algorithm is evaluated mainly on malware data sets, and results are compared with unmodified Random Forests. The empirical results suggest that the proposed Random Forests, i.e., with CDM performs better than the original Random Forests.

Place, publisher, year, edition, pages
2015. Vol. 9432, 347-358 p.
Series
Machine Learning, Optimization, and Big Data, ISSN 0302-9743 ; 9432
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-12949DOI: 10.1007/978-3-319-27926-8_31OAI: oai:DiVA.org:bth-12949DiVA: diva2:955645
Conference
International Workshop on Machine learning, Optimization and big Data, Taormina, Sicily
Available from: 2016-08-25 Created: 2016-08-25 Last updated: 2017-01-19Bibliographically approved

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Shahzad, Raja KhurramLavesson, NiklasBoldt, Martin
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