AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction
- https://doi.org/10.2991/ijcis.2018.125905686How to use a DOI?
- kNN, Deep learning, Autoencoders, Dimensionality reduction, High dimensionality
High dimensionality tends to be a challenge for most machine learning tasks, including classification. There are different classification methodologies, of which instance-based learning is one. One of the best known members of this family is the k-nearest neighbors (kNNs) algorithm. Its strategy relies on searching a set of nearest instances. In high-dimensional spaces, the distances between examples lose significance. Therefore, kNN, in the same way as many other classifiers, tends to worsen its performance as the number of input variables grows. In this study, AEkNN, a new kNN-based algorithm with built-in dimensionality reduction, is presented. Aiming to obtain a new representation of the data, having a lower dimensionality but with more informational features, AEkNN internally uses autoencoders. From this new vector of features the computed distances should be more significant, thus providing a way to choose better neighbors. An experimental evaluation of the new proposal is conducted, analyzing several configurations and comparing them against the original kNN algorithm and classical dimensionality reduction methods. The obtained conclusions demonstrate that AEkNN offers better results in predictive and runtime performance.
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - JOUR AU - Francisco J. Pulgar AU - Francisco Charte AU - Antonio J. Rivera AU - María J. del Jesus PY - 2019 DA - 2019/02 TI - AEkNN: An AutoEncoder kNN-Based Classifier With Built-in Dimensionality Reduction JO - International Journal of Computational Intelligence Systems SP - 436 EP - 452 VL - 12 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.2018.125905686 DO - https://doi.org/10.2991/ijcis.2018.125905686 ID - Pulgar2019 ER -