Improvement to a Neural Network Cloud Classifier

Richard L. Bankert Naval Research Laboratory, Monterey, California

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David W. Aha Naval Research Laboratory, Washington, D.C.

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Abstract

Examination of various feature selection algorithms has led to an improvement in the performance of a probabilistic neural network (PNN) cloud classifier. Thee algorithms reduce the number of network inputs by eliminating redundant and/or irrelevant features (spectral, textural, and physical measurements). One such algorithm, selecting 11 of the 204 total features, provides a 7% increase in PNN overall accuracy compared to an earlier version using 15 features. This algorithm employs the same search procedure as before, but a different evaluation function than used previously, which provides a similar bias to that of the PNN classifier. Noticeable accuracy improvements were also evident in individual cloud-pipe classes.

Abstract

Examination of various feature selection algorithms has led to an improvement in the performance of a probabilistic neural network (PNN) cloud classifier. Thee algorithms reduce the number of network inputs by eliminating redundant and/or irrelevant features (spectral, textural, and physical measurements). One such algorithm, selecting 11 of the 204 total features, provides a 7% increase in PNN overall accuracy compared to an earlier version using 15 features. This algorithm employs the same search procedure as before, but a different evaluation function than used previously, which provides a similar bias to that of the PNN classifier. Noticeable accuracy improvements were also evident in individual cloud-pipe classes.

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