| Title |
Design of Fuzzy k-Nearest Neighbors Classifiers based on Feature Extraction by using Stacked Autoencoder |
| Authors |
노석범(Rho, Suck-Bum) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2015.64.1.113 |
| Keywords |
Stacked Autoencoders ; Boltzmann Machine ; Restrict Boltzmann Machine ; Deep Networks ; Fuzzy C-Means Clustering ; Fuzzy k-Nearest Neighbors |
| Abstract |
In this paper, we propose a feature extraction method using the stacked autoencoders which consist of restricted Boltzmann machines. The stacked autoencoders is a sort of deep networks. Restricted Boltzmann machines (RBMs) are probabilistic graphical models that can be interpreted as stochastic neural networks. In terms of pattern classification problem, the feature extraction is a key issue. We use the stacked autoencoders networks to extract new features which have a good influence on the improvement of the classification performance. After feature extraction, fuzzy k-nearest neighbors algorithm is used for a classifier which classifies the new extracted data set. To evaluate the classification ability of the proposed pattern classifier, we make some experiments with several machine learning data sets. |