Review of Input Variable Selection Methods for Artificial Neural Networks

作者：Robert J. May, Graeme C. Dandy, Holger R. Maier · 发表于：InTech eBooks · 年份：2011 · DOI：10.5772/16004 · 被引用次数：380 · 研究领域：Neural Networks and Applications、Fault Detection and Control Systems、Hydrological Forecasting Using AI

The choice of input variables is a fundamental, and yet crucial consideration in identifying the optimal functional form of statistical models. The task of selecting input variables is common to the development of all statistical models, and is largely dependent on the discovery of relationships within the available data to identify suitable predictors of the model output. In the case of parametric, or semi-parametric empirical models, the difficulty of the input variable selection task is somewhat alleviated by the a priori assumption of the functional form of the model, which is based on some physical interpretation of the underlying system or process being modelled. However, in the case of artificial neural networks (ANNs), and other similarly data-driven statistical modelling approaches, there is no such assumption made regarding the structure of the model. Instead, the input variables are selected from the available data, and the model is developed subsequently. The difficulty of selecting input variables arises due to (i) the number of available variables, which may be very large; (ii) correlations between potential input variables, which creates redundancy; and (iii) variables that have little or no predictive power. Variable subset selection has been a longstanding issue in fields of applied statistics dealing with inference and linear regression (Miller, 1984), and the advent of ANN models has only served to create new challenges in this field. The non-linearity, inh...