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Input selection for nonlinear regression models
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DOI：10.1109/TFUZZ.2004.834810.png)
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Abstract
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A simple and effective method for the selection of significant inputs in nonlinear regression models is proposed. Given a set of input-output data and an initial superset of potential inputs, the relevant inputs are selected by checking whether after deleting a particular input, the data set is still consistent with the basic property of a function. In order to be able to handle real-valtied and noisy data in a sensible manner, fuzzy clustering is first applied. The obtained clusters are compared by using a similarity measure in order to find inconsistencies within the data. Several examples using simulated and real-world data sets are presented to demonstrate the effectiveness of the algorithm.
Keywords:
fuzzy clustering
fuzzy modeling
input selection
regression models
similarity measures
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