Consensus decision making in random forests
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)
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.
Machine Learning, Optimization, and Big Data, ISSN 0302-9743 ; 9432
IdentifiersURN: urn:nbn:se:bth-12949DOI: 10.1007/978-3-319-27926-8_31OAI: oai:DiVA.org:bth-12949DiVA: diva2:955645
International Workshop on Machine learning, Optimization and big Data, Taormina, Sicily