01779nas a2200217   4500000000100000000000100001008004100002260001200043653002200055653002400077653003300101653002400134100001500158700002300173245010200196856009600298300001200394490000600406520113500412022001401547       2018                            d  c12/201810aFeature Selection10aHigh Dimensionality10aIntuitionistic Fuzzy Entropy10aText Categorization1 aB S Harish1 aM B Revanasiddappa00aA New Feature Selection Method based on Intuitionistic Fuzzy Entropy to Categorize Text Documents  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/04/ijimai_5_3_12_pdf_16348.pdf  a106-1170 v53 aSelection of highly discriminative feature in text document plays a major challenging role in categorization. Feature selection is an important task that involves dimensionality reduction of feature matrix, which in turn enhances the performance of categorization. This article presents a new feature selection method based on Intuitionistic Fuzzy Entropy (IFE) for Text Categorization. Firstly, Intuitionistic Fuzzy C-Means (IFCM) clustering method is employed to compute the intuitionistic membership values. The computed intuitionistic membership values are used to estimate intuitionistic fuzzy entropy via Match degree. Further, features with lower entropy values are selected to categorize the text documents. To find the efficacy of the proposed method, experiments are conducted on three standard benchmark datasets using three classifiers. F-measure is used to assess the performance of the classifiers. The proposed method shows impressive results as compared to other well known feature selection methods. Moreover, Intuitionistic Fuzzy Set (IFS) property addresses the uncertainty limitations of traditional fuzzy set.  a1989-1660