MNIST Dataset Classification Utilizing k-NN Classifier with Modified Sliding Window Metric

09/18/2018

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by   Behrad Toghi, et al.

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This paper evaluates the performance of the K-nearest neighbor classification algorithm on the MNIST dataset of the handwritten digits. The L2 Euclidean distance metric is compared to a modified distance metric which utilizes the sliding window technique in order to avoid performance degradations due to slight spatial misalignments. Accuracy and confusion matrix 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 simple method.