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http://dx.doi.org/10.5391/JKIIS.2006.16.6.736

An Adaptive Classification Model Using Incremental Training Fuzzy Neural Networks  

Rhee, Hyun-Sook (동양공업전문대학 전산정보학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.16, no.6, 2006 , pp. 736-741 More about this Journal
Abstract
The design of a classification system generally involves data acquisition module, learning module and decision module, considering their functions and it is often an important component of intelligent systems. The learning module provides a priori information and it has been playing a key role for the classification. The conventional learning techniques for classification are based on a winner take all fashion which does not reflect the description of real data where boundarues might be fuzzy Moreover they need all data for the learning of its problem domain. Generally, in many practical applications, it is not possible to prepare them at a time. In this paper, we design an adaptive classification model using incremental training fuzzy neural networks, FNN-I. To have a more useful information, it introduces the representation and membership degree by fuzzy theory. And it provides an incremental learning algorithm for continuously gathered data. We present tie experimental results on computer virus data. They show that the proposed system can learn incrementally and classify new viruses effectively.
Keywords
Incremental Training; Fuzzy Neural Networks; Classification; Computer Virus;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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