Optimization of Random Forest Hyperparameter Using Improved PSO for Handwritten Digits Classification

In machine learning, classification is one of the important methods used to train a model for the identification of each class on a labelled dataset. The performance of the algorithm can be enhanced using ensemble techniques such as bagging and random subsampling. Random forest (RF) is an ensemble machine learning algorithm to address multiclass classification problems. Several RF algorithms have been proposed to obtain various levels of accuracy on different datasets during classification. But it has been observed that the manual configuration of RF hyperparameter and its accuracy varies significantly depending upon different datasets. Manual configuration of hyperparameter is a time consuming job. Thus, the hyperparameter optimization process is used to generate optimal hyperparameter efficiently. Hyperparameter optimization also enhances the performance of machine learning algorithms. This paper proposes improved particle swarm optimization (PSO) method as hyperparameter optimizer to identify the optimal hyperparameter of the random forest algorithm. We compare the performance of RF model using improved PSO optimizer and existing RF model with default hyperparameter. The improved PSO outperforms the improvement of accuracy during the classification of each digit of the handwritten dataset.