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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Consensus Decision Making in Random Forests
verfasst von : Raja Khurram Shahzad, Mehwish Fatima, Niklas Lavesson, Martin Boldt
Erschienen in: Machine Learning, Optimization, and Big Data
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Abstract
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.