The effects of linguistic hedges (LHs) on neuro-fuzzy classifier are shown in Part 1. This paper presents a fuzzy feature selection (FS) method based on the LH concept. The values of LHs can be used to show the importance degree of fuzzy sets. When this property is used for classification problems, and every class is defined by a fuzzy classification rule, the LHs of every fuzzy set denote the importance degree of input features. If the LHs values of features are close to concentration values, these features are more important or relevant, and can be selected. On the contrary, if the LH values of features are close to dilation values, these features are not important, and can be eliminated. According to the LHs value of features, the redundant, noisily features can be eliminated, and significant features can be selected. For this aim, a new LH-based FS algorithm is proposed by using adaptive neuro-fuzzy classifier (ANFC).