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Enhancement of Random Forest by Utilizing Modified Whale Optimization Algorithm



Ali Ojiram G. Pirzada, Renz Michael M. Leandicho, Mark Christopher R. Blanco, Raymund M. Dioses, and Vivien A. Agustin, 2023. Enhancement of Random Forest by Utilizing Modified Whale Optimization Algorithm. United International Journal for Research & Technology (UIJRT), 4(8), pp61-71.


Machine learning (ML) has various applications, including the ability of software to predict and analyze results more correctly without explicit instructions, identify the best ways to automate tasks, enhance processes, and many other things. The Random Forest (RF) model has been proven to perform well and has applications in many different sectors, but current research suggests that there is still room for improvement. It is the most well-known and often used machine learning technique. There is still room for development with the RF model. In this paper, the researchers provided an optimization algorithm (WOA) to enhance and improve the accuracy of the Random Forest Algorithm on a UNSW-NB15 Intrusion detection dataset. It achieved an accuracy of 97.14% with the hybrid algorithm compared to the traditional algorithm of 94.79%. Furthermore, the recall scores for the proposed algorithm and traditional RF were 95.80% and 92.26%, respectively, while the precision scores for MWOA-RF and traditional RF were equal at 1.000. It indicates that the suggested method performed better at correctly identifying positive cases and had a lower rate of false negatives recognized. Lastly, The F1-Score given by the MWOA-RF is 0.9785 compared to the F1-Score of the traditional RF, which is 0.9597, which signifies that the proposed MWOA-RF performs better for classification and is the better model of the two since its value is closer to 1.

Keywords: Machine Learning, Metaheuristic, Random Forest, Whale Optimization Algorithm, Overfitting, Hyperparameter tuning, Feature Selection, Classification, Confusion Matrix, Precision, Recall.

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