Improving the Random Forest Algorithm by Randomly Varying the Size of the Bootstrap Samples for Low Dimensional Data Sets
Nasim Adnan, Md Zahidul Islam · Charles Sturt University Research Output (CRO) · 2015
The Random Forest algorithm generates quite diverse decision trees as the base classifiers for high dimensional data sets. However, for low dimensional data sets the diversity among the trees falls sharply. In Random Forest, the size of the bootstrap samples generally remains the same every time to generate a decision tree as the base classifier. In this paper we propose to vary the size of the bootstrap samples randomly within a predefined range in order to increase diversity among the trees. We conduct an elaborate experimentation on several low dimensional data sets from UCI Machine Learning Repository. The experimental results show the effectiveness of our proposed technique.