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PAKDD
1998
ACM

Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks

8 years 7 months ago
Data Mining Using Dynamically Constructed Recurrent Fuzzy Neural Networks
Abstract. Approaches to data mining proposed so far are mainly symbolic decision trees and numerical feedforward neural networks methods. While decision trees give, in many cases, lower accuracy compared to feedforward neural networks, the latter show black-box behaviour, long training times, and difficulty to incorporate available knowledge. We propose to use an incrementally-generated recurrent fuzzy neural network which has the following advantages over feedforward neural network approach: ability to incorporate existing domain knowledge as well as to establish relationships from scratch, and shorter training time. The recurrent structure of the proposed method is able to account for temporal data changes in contrast to both both feedforwardneural network and decision tree approaches. It can be viewed as a gray box which incorporates best features of both symbolic and numerical methods. The effectiveness of the proposed approach is demonstrated by experimental results on a set of st...
Yakov Frayman, Lipo Wang
Added 06 Aug 2010
Updated 06 Aug 2010
Type Conference
Year 1998
Where PAKDD
Authors Yakov Frayman, Lipo Wang
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