MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding-window Metric
This work addresses classification accuracy for digit recognition, but it is incremental as it modifies an existing method on a well-known dataset.
The paper tackled the problem of classifying handwritten digits in the MNIST dataset by comparing a standard k-NN classifier with Euclidean distance to an enhanced version using a sliding-window metric to reduce errors from spatial misalignments, resulting in significant accuracy improvements.
The MNIST dataset of the handwritten digits is known as one of the commonly used datasets for machine learning and computer vision research. We aim to study a widely applicable classification problem and apply a simple yet efficient K-nearest neighbor classifier with an enhanced heuristic. We evaluate the performance of the K-nearest neighbor classification algorithm on the MNIST dataset where the $L2$ Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradation due to slight spatial misalignments. The accuracy metric and confusion matrices are used as the performance indicators to compare the performance of the baseline algorithm versus the enhanced sliding window method and results show significant improvement using this proposed method.