Complement random forest

Random Forest is a popular decision forest building algorithm which focuses on generating diverse deci-sion trees as the base classifiers. For high dimensional data sets, Random Forest generally excels in generat-ing 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 gen-erate accurate yet diverse decision trees when applied on high dimensional data sets. We conduct an elab-orate experimental analysis on seven publicly avail-able data sets from UCI Machine Learning Reposi-tory. The experimental results indicate the effective-ness of our proposed technique.