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- W2099170171 abstract "Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspec-tral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which increases the chance of avoiding locally optimum solutions. Second, we exploit local cross correlation pair-wise amongst classes of interest to select suitable features for class discrimination. Third, we adopt the concept of distributed spacing from the multi-objective optimization community to distribute features across the spectrum in order to select less correlated features. The classification performance of our semi-embedded feature selection and classification method is demonstrated on a 12-class textile hyperspectral classification problem under several noise realizations. These results are compared with a variety of feature selection methods that cover a broad range of approaches." @default.
- W2099170171 created "2016-06-24" @default.
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- W2099170171 date "2010-06-01" @default.
- W2099170171 modified "2023-09-23" @default.
- W2099170171 title "Stochastic feature selection with distributed feature spacing for hyperspectral data" @default.
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- W2099170171 doi "https://doi.org/10.1109/whispers.2010.5594951" @default.
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