Training Algorithm for Dendrite Morphological Neural Network Using K-Medoids
Yisti Vita Via, Chrystia Aji Putra, Ronggo Alit
Yisti Vita Via
Available Online December 2018.
- https://doi.org/10.2991/icst-18.2018.99How to use a DOI?
- pattern classification; Neural Networks; Dendrite Morphological; K-medoids; training algorithm
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icst-18.2018.99 ID - Via2018/12 ER -