Feature Selection using ReliefF Algorithm

Abstract: Feature Selection is the preprocessing process of identifying the subset of data from large dimension data. To identifying the required data, using some Feature Selection algorithms. Like Relief, Parzen-Relief algorithms, it attempts to directly maximize the classification accuracy and naturally reflects the Bayes error in the objective. In this paper a new algorithm is proposed determine feature selection with error minimization. Proposed algorithmic framework selects a subset of features by minimizing the Bayes error rate estimated by a nonparametric estimator.

Keywords: Feature Selection; ReliefF; Image processing.