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CIFAR-10: KNN-based Ensemble of Classifiers

2016-11-15Code Available0· sign in to hype

Yehya Abouelnaga, Ola S. Ali, Hager Rady, Mohamed Moustafa

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Abstract

In this paper, we study the performance of different classifiers on the CIFAR-10 dataset, and build an ensemble of classifiers to reach a better performance. We show that, on CIFAR-10, K-Nearest Neighbors (KNN) and Convolutional Neural Network (CNN), on some classes, are mutually exclusive, thus yield in higher accuracy when combined. We reduce KNN overfitting using Principal Component Analysis (PCA), and ensemble it with a CNN to increase its accuracy. Our approach improves our best CNN model from 93.33% to 94.03%.

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