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 feedforward neural 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 standard data mining problems.
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Frayman, Y., Wang, L. (1998). Data mining using dynamically constructed recurrent fuzzy neural networks. In: Wu, X., Kotagiri, R., Korb, K.B. (eds) Research and Development in Knowledge Discovery and Data Mining. PAKDD 1998. Lecture Notes in Computer Science, vol 1394. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64383-4_11
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DOI: https://doi.org/10.1007/3-540-64383-4_11
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