Abstract

Designing and operating a classification system becomes drastically more difficult as the data dimensionality increases. A feature extraction (FE) step is often used to reduce the data dimensionality to mitigate this complexity. Thus FE may be viewed as a form of data compression whos objective is to minimize the consequences reducing the dimensionality has on class separability. This differs from the normal objective of data compression which is to minimize distortion, typically measured in the mean squared sense. It is often unclear whether the resulting features from a FE method provide an optimum set for classification. Further, extracting discrimination features from finite data sets increases in difficulty as the dimensionality of the data increases. The need for features to reduce complexity, combined with the difficulties of extracting features, justifies the need for studying ways of ranking feature sets for classification, i.e. feature set evaluation (FSE) techniques. This ...

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