One-Vs-All Binarization Technique in the Context of Random Forest

Binarization techniques are widely used to solve multi-class classification problems. These techniques reduce the classification complexity of multi-class classification problems by dividing the original data set into two-class segments or replicas. Then a set of simpler classifiers are learnt from the two-class segments or replicas. The outputs from these classifiers are combined for final classification. Binarization can improve prediction accuracy when compared to a single classifier. However, to be declared as a superior technique, binarization techniques need to prove themselves in the context of ensemble classifiers such as Random Forest. Random Forest is a state-of-the-art popular decision forest building algorithm which focuses on generating diverse decision trees as the base classifiers. In this paper we evaluate one-vs-all binarization technique in the context of Random Forest. We present an elaborate experimental result involving ten widely used data sets from the UCI Machine Learning Repository. The experimental results exhibit the effectiveness of one-vs-all binarization technique in the context of Random Forest.