Abstract
This paper proposes a new similarity score known as k-NN-SSc that is incorporated into the traditional k-NN classifier. The proposed similarity score computes the similarity between two instances, and the score is used to find the k-nearest neighbors of an unknown instance for the k-NN classifier. The effectiveness of the proposed k-NN-SSc similarity score measure is compared with Euclidean, Minkowski, Manhattan, Chebyshev, and Cosine distance measures. From the experimental results, we observed that the proposed measure performs better as compared to other competing measures in terms of Accuracy, F1 Score, and Matthew’s Correlation Coefficient (MCC) on 20 UCI repository datasets.
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Khumukcham, R.S., Takhellambam, L., Urikhimbam, B.C., Yambem, R., Hoque, N. (2023). k-NN-SSc: An Effective Similarity Score for k-NN Classifier. In: Borah, S., Gandhi, T.K., Piuri, V. (eds) Advanced Computational and Communication Paradigms . ICACCP 2023. Lecture Notes in Networks and Systems, vol 535. Springer, Singapore. https://doi.org/10.1007/978-981-99-4284-8_4
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