NDLI: A rule-based filter network for multiclass data classification

Content Provider	IEEE Xplore Digital Library
Author	Tusor, B. Varkonyi-Koczy, A.R.
Copyright Year	2015
Description	Author affiliation: Inst. of Mechatron. & Vehicle Eng., Obuda Univ., Budapest, Hungary (Varkonyi-Koczy, A.R.) \|\| Doctoral Sch. of Appl. Inf., Obuda Univ., Budapest, Hungary (Tusor, B.)
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.
Starting Page	1102
Ending Page	1107
File Size	953631
Page Count	6
File Format	PDF
ISBN	9781479961146
DOI	10.1109/I2MTC.2015.7151425
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-11
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods Neurons Reinforced learning Clustering Fuzzy control system Training Accuracy Supervised learning Training data Clustering algorithms Computer architecture Classification Fuzzy inference systems Radial base function networks
Content Type	Text
Resource Type	Article

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