On the use of the Genetic Algorithm Filter-based Feature Selection Technique for Satellite Precipitation Estimation
Mahrooghy, M., Younan, N. H., Anantharaj, V. G., Aanstoos, J.V., & Yarahmadian, S. (2012). On the use of the Genetic Algorithm Filter-based Feature Selection Technique for Satellite Precipitation Estimation. IEEE Geoscience and Remote Sensing Letters. 9(5), 963-967.
A feature selection technique is used to enhance the
precipitation estimation from remotely sensed imagery using an
artificial neural network (PERSIANN) and cloud classification
system (CCS) method (PERSIANN-CCS) enriched by wavelet features.
The feature selection technique includes a feature similarity
selection method and a filter-based feature selection using genetic
algorithm (FFSGA). It is employed in this study to find an optimal
set of features where redundant and irrelevant features are
removed. The entropy index fitness function is used to evaluate the
feature subsets. The results show that using the feature selection
technique not only improves the equitable threat score by almost
7% at some threshold values for the winter season, but also it
extremely decreases the dimensionality. The bias also decreases in
both the winter (January and February) and summer (June, July,
and August) seasons.