Recently, feature selection have been applied to many area which have housands of dataset. Those area is text categorization , microarrays , biomedical signal analysis.
Feature selection in pattern recognition and machine learning to play a crucial role. Law in a number of feature selection method, the sequential forward search (SFS) and sequential backward search (SBS) is the most widely used method, the proposed method is based on proximity combine in sequential forward search method (SFS), as well as sequential backward search (SBS), we have developed a feature selection based on the concept of proximity, in accordance with the characteristics (one-dimensional data) before and after each feature or features (2D data), the relationship between up and down to give the distance weight value, ranking, based on its distance weight value according to the order to select the recognition rate can improve the characteristics, and then the rest of the unimportant features further be removed, so filter out the important feature subset in order to enhance the recognition rate, and characteristics will be gathered at a critical the area.
In this paper, the experimental part we were had simulation test for character recognition, as well as rat brain waves in the rat brain waves for the rats awake state (AW), slow wave sleep (SWS) and rapid eye movement sleep (REM) brainwave state of the three acts of the state to do feature selection. In addition, character recognition, text "太", "大", "犬" experiment, respectively, as a group of "太"大"and"大","犬"a group of too, "太" and"大"and"犬"group. Finally the proposed method in this paper the above experiments to enhance the recognition rate and are looking out feature subset which fall in the critical region.