Training Algorithm for Dendrite Morphological Neural Network Using K-Medoids

DOI

10.2991/icst-18.2018.99How to use a DOI?

Keywords

pattern classification; Neural Networks; Dendrite Morphological; K-medoids; training algorithm

Abstract

Pattern classification is one of the relevant problems in Artificial Intelligence. Neural networks have been studied as one of the most successful methods for pattern classification. Classical perceptron can only solve linear classification problems. Morphological Neural Networks (MNN) is an alternative way to solve classification problems in the form of linear and nonlinear. Dendrite Morphological Neural Networks (DMNN) is introduced as an improved method of classical MNN. The important problem that occurs in the DMNN training algorithm is to cluster objects with hyper boxes and classify each in the corresponding class. This paper presents the proposed training algorithm using K-medoids clustering algorithm to create the hyper boxes in the dimensional space. K-medoids is better than other clustering methods in execution time and not sensitive to outliers. The implementation of the proposed algorithm will be involved in various simulations using artificial data sets and compared with other methods to evaluate the performance of this method in future work.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Yisti Vita Via
AU  - Chrystia Aji Putra
AU  - Ronggo Alit
PY  - 2018/12
DA  - 2018/12
TI  - Training Algorithm for Dendrite Morphological Neural Network Using K-Medoids
BT  - Proceedings of the International Conference on Science and Technology (ICST 2018)
PB  - Atlantis Press
SP  - 476
EP  - 480
SN  - 2589-4943
UR  - https://doi.org/10.2991/icst-18.2018.99
DO  - 10.2991/icst-18.2018.99
ID  - Via2018/12
ER  -