Ummay Maria Muna, Shanta Biswas, Syed Abu Ammar Muhammad Zarif, Dewan Md. Farid
Random Forest is one of the most popular supervised learning ensemble methods in machine learning. Random Forest
engenders a set of random trees and considers majority voting technique to classify known and unknown data instances. In
Random Forest, decision tree induction is used as a baseline classifier. Decision tree is a top-down divide and conquer
recursive algorithm that applies feature selection technique to select the root/best feature e.g. ID3 (Iterative
Dichotomiser 3), C4.5 (an extension ID3), and CART (Classification and Regression Tree). In this paper, we have
proposed a new approach to improve the performance of Random Forest classifier using clustering technique. This proposed
idea can be applied for Big Data mining.
First, we have clustered the data into several clusters using K-Means Clustering and then apply the Random Forest technique in each cluster. We have tested the proposed idea with existing classical Random Forest technique and found proposed Random Forest technique performs better than traditional Random Forest algorithm on five datasets taken from UCI Machine Learning Repository.