Improving the performance of neural networks in classification using fuzzy linear regression

Abstract In this paper, we apply the fuzzy linear regression (FLR) with fuzzy intervals analysis into a neural network classification model. The FLR works as a data handler and separates the data sample into two groups. By training two independent neural works with these two groups, we can better describe the distribution space of the corresponding data sample with two different functions, rather than using only one function. The experimental result shows that our approach improves the accuracy of classification.

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