K Nearest Neighbor (KNN) Method Used in Feature Selection
Yao Li · Jisuanji yu yingyong huaxue · 2001
Feature selection is a key step of data processing using pattern recognition approaches. And the data processed can be roughly divided into two types: one side type and inclusion type. Because the difference of the special distribution of samples of these two types of data, the methods used to select feature in these two types of data should be different. However, some traditional methods,such as Principal Component Regression (PCA), Partial Least Square (PLS) and so on, are usually just applicable to the one side type data. Here, the K\|Nearest Neighbor (KNN) method, one of commonly used pattern recognition classification method, is introduced for the purpose of feature selection. Practice of computation indicates that this method is not only can be used to feature selection in one\|side type data, but also more suitable than many traditional methods of feature selection when data structure is inclusion type.