Logarithmic Spiral-based Construction of RBF Classifiers

Clustering process is defined as grouping similar objects together into homogeneous groups or clusters. Objects that belong to one cluster should be very similar to each other, but objects in different clusters will be dissimilar. It aims to simplify the representation of the initial data. The automatic classification recovers all the methods allowing the automatic construction of such groups. This paper describes the design of radial basis function (RBF) neural classifiers using a new algorithm for characterizing the hidden layer structure. This algorithm, called k-means Mahalanobis distance, groups the training data class by class in order to calculate the optimal number of clusters of the hidden layer, using two validity indexes. To initialize the initial clusters of k-means algorithm, the method of logarithmic spiral golden angle has been used. Two real data sets (Iris and Wine) are considered to improve the efficiency of the proposed approach and the obtained results are compared with basic literature classifier

@article{Guerfala2017,
  title     = {Logarithmic Spiral-based Construction of RBF Classifiers},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {2},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Wajih Guerfala and Amel Sifaoui and Afef Abdelkrim},
  doi       = {10.14569/IJACSA.2017.080235},
  url       = {https://doi.org/10.14569/IJACSA.2017.080235}
}