Relief´s Application in Handwriting Recognition

Relief is a feature subset selection algorithm, which provides a way to select an optimal feature set by maximum hypothesis-margin. Especially, it can rapidly deals with high-dimensionality feature selection. This paper mainly studies how the multi-class and unbalance data circumstance affect the algorithm process. We suggest an extension - ReliefF* and apply it in the Chinese handwriting recognition. The new algorithm not only saves computing time, but also results in a substantial improvement in the classification accuracy. The experiment results indicate ReliefF* is more effective for small-sample classification tasks