Combining Minkowski and Chebyshev: New distance proposal and survey of distance metrics using k-nearest neighbours classifier
This work addresses the need for faster and more accurate distance metrics in machine learning tasks like classification, but it is incremental as it builds on existing distance metrics without introducing a new paradigm.
This paper tackles the problem of improving efficiency and accuracy in distance-based classification by proposing a new distance metric that combines Minkowski and Chebyshev distances. The result is a method that is approximately 1.3 times faster than Manhattan distance and 329.5 times faster than Euclidean distance in discrete neighborhood iterations, and it achieved better-than-average accuracy in 26 out of 33 datasets and best accuracy in 9 out of 33 cases when used with a k-NN classifier.
This work proposes a distance that combines Minkowski and Chebyshev distances and can be seen as an intermediary distance. This combination not only achieves efficient run times in neighbourhood iteration tasks in Z^2, but also obtains good accuracies when coupled with the k-Nearest Neighbours (k-NN) classifier. The proposed distance is approximately 1.3 times faster than Manhattan distance and 329.5 times faster than Euclidean distance in discrete neighbourhood iterations. An accuracy analysis of the k-NN classifier using a total of 33 datasets from the UCI repository, 15 distances and values assigned to k that vary from 1 to 200 is presented. In this experiment, the proposed distance obtained accuracies that were better than the average more often than its counterparts (in 26 cases out of 33), and also obtained the best accuracy more frequently (in 9 out of 33 cases).