FEATURE SELECTION USING AN EFFECTIVE DIMENSIONALITY REDUCTION TECHNIQUE?

processing applications with a large number of dimensions has been a challenge to the KDD (Knowledge Discovery and Data mining) community. An effective dimensionality reduction technique is an essential pre-processing method to remove noisy features. The proposed combined method for feature selection, where a filter based on correlation is applied on whole features set to find the relevant ones, and then, on these features a wrapper is applied in order to find the best features subset for a specified predictor.