A Hybrid Model for Predicting Classification Dataset based on Random Forest, Support Vector Machine and Artificial Neural Network

Abstract

Machine Learning offers a rich array of algorithms, and the performance of these algorithms can vary significantly depending on the specific task. Combining these traditional algorithms can lead to the development of innovative hybrid structures that outperform individual models. One such novel hybrid model is the Hybrid Support Random Forest Neural Network (HSRFNN), which is designed to deliver enhanced performance and accuracy. HSRFNN represents a fusion of Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) to leverage their respective strengths. This hybrid model consistently outperforms the individual models of Random Forest, SVM, and ANN. In this study, ten diverse datasets sourced from UCI and Kaggle data repositories were considered for evaluation. The accuracy of the HSRFNN model was meticulously compared with the three traditional algorithms, namely Random Forest, Support Vector Machine, and Artificial Neural Network. Various accuracy metrics, such as Correctly Classified Instances (CCI), Incorrectly Classified Instances (ICI), Accuracy (A), and Time Taken to Build Model (TTBM), were used for the comparative analysis. This research strives to demonstrate that HSRFNN, through its hybrid architecture, can offer superior accuracy and performance compared to individual algorithms. The choice of datasets from different sources enhances the generalizability of the results, making HSRFNN a promising approach for a wide range of machine learning tasks. Further exploration and fine-tuning of HSRFNN may unlock its potential for even more challenging and diverse datasets.