Feature selection algorithm for uncertain text classification - WANG Bo; JIA Yan; YANG Shu-qiang; ZHOU Bin (Tongxin xuebao)
A novel algorithm called FSUNT was proposed based on HSIC, with the focus on the vagueness and uncertainty which might be taken into account during feature selection. For text data with fixed feature values and uncertain class labels, features were ranked according to the correlation between features and uncertain class labels evaluated by HSIC. The results of experimental evaluation on a variety of datasets show better performance and stability of FSUNT.