, First, we fix one element of the pair as a nearest neighbor in the class of the item to be classified. Then, we fix the second one as a most remote element: this leads to a degraded accuracy. Finally, we consider the second element as a second nearest neighbor in the class. Our experiments on UCI benchmarks show that we are still competitive with regard to state of the art classifiers (k-NN, SVM) while having drastically decreased the complexity. Indeed, as datasets become bigger and bigger, the scalability of oddness-based classifiers is paramount: this has to be further investigated in future works. While using the classical notion of nearest neighbor, our handling of them is quite different from the one in k-NN methods
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