Complete Gini-Index Text (GIT) feature-selection algorithm for text classification

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 va...

Full description

Saved in:
Bibliographic Details
Published in2010 2nd International Conference on Software Engineering and Data Mining pp. 366 - 371
Main Authors Heum Park, Soonho Kwon, Hyuk-Chul Kwon
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2010
Subjects
Artificial intelligence
Classification algorithms
Computer science
Entropy
feature selection
Gini-Index
Information filtering
Information filters
Mutual information
Support vector machine classification
Support vector machines
Text categorization
text classification
Online AccessGet full text
ISBN1424473241
9781424473243

Cover

More Information
Summary: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.
ISBN:1424473241
9781424473243