Complement Random Forest

Adnan, M.N. and Islam, M.Z.

    Random Forest is a popular decision forest building algorithm which focuses on generating diverse decision trees as the base classifiers. For high dimensional data sets, Random Forest generally excels in generating diverse decision trees at the cost of less accurate individual decision trees. To achieve higher prediction accuracy, a decision forest needs both accurate and diverse decision trees as the base classifiers. In this paper we propose a novel decision forest algorithm called Complement Random Forest that aims to generate accurate yet diverse decision trees when applied on high dimensional data sets. We conduct an elaborate experimental analysis on seven publicly avail- able data sets from UCI Machine Learning Repository. The experimental results indicate the effectiveness of our proposed technique.
Cite as: Adnan, M.N. and Islam, M.Z. (2015). Complement Random Forest. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 89-97
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