k-Nearest Neighbors by Means of Sequence to Sequence Deep Neural Networks and Memory Networks
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k-Nearest Neighbors is one of the most fundamental but effective classification models. In this paper, we propose two families of models built on a sequence to sequence model and a memory network model to mimic the k-Nearest Neighbors model, which generate a sequence of labels, a sequence of out-of-sample feature vectors and a final label for classification, and thus they could also function as oversamplers. We also propose 'out-of-core' versions of our models which assume that only a small portion of data can be loaded into memory. Computational experiments show that our models outperform k-Nearest Neighbors due to the fact that our models must produce additional output and not just the label. As oversamples on imbalanced datasets, the models often outperform SMOTE and ADASYN.
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