A rule-based filter network for multiclass data classification

Author

Tusor, Balazs ; Varkonyi-Koczy, Annamaria R.

Author_Institution

Doctoral Sch. of Appl. Inf., Obuda Univ., Budapest, Hungary

fYear

2015

fDate

11-14 May 2015

Firstpage

1102

Lastpage

1107

Abstract

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.

Keywords

inference mechanisms; learning (artificial intelligence); pattern classification; pattern clustering; clustering methods; machine learning problem; multiclass data classification; network functionality; radial basis function networks; rule-based filter network; rule-based inference network; Accuracy; Clustering algorithms; Clustering methods; Computer architecture; Neurons; Training; Training data; classification; clustering; fuzzy control system; fuzzy inference systems; radial base function networks; reinforced learning; supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Instrumentation and Measurement Technology Conference (I2MTC), 2015 IEEE International

Conference_Location

Pisa

Type

conf

DOI

10.1109/I2MTC.2015.7151425

Filename

7151425