Abstract:
This study investigates methods to enhance the accuracy of Convolutional Neural Net works (CNNs) through the application of Boosting Ensemble methodologies. The re search covers diverse image datasets, including CIFAR-10, CIFAR-100, and Fashion
MNIST, aiming to utilize AdaBoost, a widely adopted boosting algorithm, to enhance
CNN performance.
A crucial aspect of this research is the assessment of AdaBoost’s effectiveness in address ing imbalanced datasets. Imbalanced datasets, marked by uneven class distributions,
pose a common challenge in machine learning. Understanding how AdaBoost addresses
these scenarios is a central focus of this study.
The empirical findings highlight AdaBoost as a valuable complementary strategy for
improving CNN accuracy, especially in cases with imbalanced class distributions. An
important observation is that the ensemble model, incorporating AdaBoost, achieves a
significant 6% higher test accuracy compared to the baseline CNN. This improvement
indicates a substantial enhancement in the model’s generalization ability to unseen data.
