Reducing prediction error by transforming input data for neural networks

Document Type

Article

Publication Date

4-1-2000

Abstract

The primary purpose of data transformation is to modify the distribution of input variables so that they can better match outputs. The performance of a neural network is often improved through data transformations. There are three existing data transformation methods: (1) Linear transformation; (2) statistical standardization; and (3) mathematical functions. This paper presents another data transformation method using cumulative distribution functions, simply addressed as distribution transformation. This method can transform a stream of random data distributed in any range to data points uniformly distributed on [0,1]. Therefore, all neural input variables can be transformed to the same ground-uniform distributions on [0,1]. The transformation can also serve the specific need of neural computation that requires all input data to be scaled to the range [-1,1] or [0,1]. The paper applies distribution transformation to two examples. Example 1 fits a cowboy hat surface because it provides a controlled environment for generating accurate input and output data patterns. The results show that distribution transformation improves the network performance by 50% over linear transformation. Example 2 is a real tunneling project, in which distribution transformation has reduced the prediction error by more than 13% compared with linear transformation.

Publication Source (Journal or Book title)

Journal of Computing in Civil Engineering

First Page

109

Last Page

116

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