A Rule-based Filter Network for Multiclass Data Classification

Nowadays, data classification is still one of the most popular fields of machine learning problems. This paper presents a new, adaptive, and easily applicable method for the solution of such problems. The method uses rules derived from the training data. The rules are processed by a rule-based inference network that is based on the classic Radial Base Function networks, with modifications in the output layer that change the functionality of the network. The training of the system, the appointing of rules is done by the clustering of the training data, for which two new clustering methods are presented and experimental results are shown in order to illustrate the efficiency of the system.