Reducing Samples Learning for Text Categorization

Abstract

Text Categorization (TC) is an important component in many information organization and information management tasks. In Text Categorization question there will be too many instances which need much computation time and memory requirement. It proposes a Generalization Capability (GC) algorithm that has the highest average generalization accuracy in these experiments, especially in the presence of uniform class noise. It also compared GC algorithm with existing reducing samples algorithms such as Condensed Nearest Neighbor, Selective Nearest Neighbor, Reduced Nearest Neighbor Rule, Edited Nearest Neighbor Rule in Text Categorization.