Complete Gini-Index Text (GIT) feature-selection algorithm for text classification
Heum Park, Soonho Kwon, Hyuk‐Chul Kwon · International Conference on Software Engineering · 2010
The recently introduced Gini-Index Text (GIT) feature-selection algorithm for text classification, through incorporating an improved Gini Index for better feature-selection performance, has some drawbacks. Specifically, the algorithm, under real-world experimental conditions, concentrates feature values to one point and be inadequate for selecting representative features. As such, good representative features cannot be estimated, and neither, moreover, can good performance be achieved in unbalanced text classification. Therefore, we suggest a new complete GIT feature-selection algorithm for text classification. The new algorithm, according to experimental results, could obtain unbiased feature values, and could eliminate many irrelevant and redundant features from feature subsets while retaining many representative features. Furthermore, the new algorithm, compared with the original version, demonstrated a notably improved overall classification performance.