Improving Classification Accuracy of Random Forest Algorithm Using Unsupervised Discretization with Fuzzy Partition and Fuzzy Set Intervals

Abstract

It is known that certain classification algorithm requires continuous data to be discretized for it to produce better classification accuracy. Hence, many works have explored the pairing of classification algorithm and discretization techniques, yet tree-based classifier especially Classification and Regression Trees (CART) still have an issue with classification accuracy regardless of different pairing with existing discretization techniques. The role of fuzzy partition and fuzzy sets interval are not something new in data discretization but none yet to explore the pairing of fuzzy discretization with tree-based algorithm. This paper will be discussing on an approach of using fuzzy based discretization and a member of tree-based algorithm known as Random Forest, a better version of CART. In this study, continuous data are identified from a dataset and discretized through the fuzzy discretization. Then, 10-fold cross validation is done on the transformed dataset and seven well-known classifiers are used including the proposed approach. Based on the results, better classification accuracy is achieved when fuzzy discretization is paired with Random Forest algorithm compared to CART. On top of that, with the present of fuzzy discretization technique, an increased in the classification accuracy has been obtained compared to other classification algorithms.