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High dimensionality, i.e. data having a large number of variables, tends tobe a challenge for most machine learning tasks, including classification. Aclassifier usually builds a model representing how a set of inputs explain theoutputs. The larger is the set of inputs and/or outputs, the more complex wouldbe that model. There is a family of classification algorithms, known as lazylearning methods, which does not build a model. One of the best known membersof this family is the kNN algorithm. Its strategy relies on searching a set ofnearest neighbors, using the input variables as position vectors and computingdistances among them. These distances loss significance in high-dimensionalspaces. Therefore kNN, as many other classifiers, tends to worse itsperformance as the number of input variables grows.  In this work AEkNN, a new kNN-based algorithm with built-in dimensionalityreduction, is presented. Aiming to obtain a new representation of the data,having a lower dimensionality but with more informational features, AEkNNinternally uses autoencoders. From this new feature vectors the computeddistances should be more significant, thus providing a way to choose betterneighbors. A experimental evaluation of the new proposal is conducted,analyzing several configurations and comparing them against the classical kNNalgorithm. The obtained conclusions demonstrate that AEkNN offers betterresults in predictive and runtime performance.

AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction