Fuzzy Hyper-line Segment Neural Network by using Association Rule Mining - Shetty, BS; Kulkarni, U.V; Sonule, Preetee M; Shinde, Manisha N (International Journal of Computer Sciences and Engineering, 2018)
In this paper, we have proposed the fuzzy hyper-line segment neural network (FHLSNN) by using association rule mining(FHLARM). Regression tree is used for pattern recognition. We have used supervised learning neural network classifier for classification of fuzzy sets. The FHLARM make the pattern classification with the help of hyper-line segments. It has two endpoints and corresponding member-ship function. The proposed model is evaluated by using iris, wine and solar mine datasets. For extraction of rules, we have used association rule mining. It gives the better classification accuracy results on various datasets as compared to previous methods. Regression tree maintains a hierarchy of extracting rules.