A Hybrid Machine Learning Model for Enhanced Classification Accuracy

Hybrid Machine Learning (HML) models have emerged as an effective solution to overcome the limitations of individual machine learning algorithms. This paper proposes a hybrid classification framework that integrates Principal Component Analysis (PCA) for dimensionality reduction with a stacked ensemble learning approach consisting of Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). The proposed model aims to enhance classification accuracy, reduce overfitting, and improve generalization. Extensive experiments conducted on a benchmark dataset demonstrate that the hybrid model outperforms traditional classifiers in terms of accuracy, precision, recall, and F1-score. The results validate the effectiveness of hybrid learning strategies for real-world classification problems.