Incorporating Topic Information in a Global Feature Selection Schema for Authorship Attribution
Authorship attribution (AA) is a stylometric analysis task of finding the author of an anonymous/disputed text document. In AA, the performance improvement of class-based feature selection schemas, such as Chi-square, and Gini index over frequency-based feature selection schemas, such as document fr...
Main Authors: | Hayri Volkan Agun, Ozgur Yilmazel |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8769823/ |
Similar Items
-
Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century
by: Andreas Neocleous, et al.
Published: (2021-01-01) -
Principal component analysis for authorship attribution
by: Amir Jamak, et al.
Published: (2012-01-01) -
Ensemble Methods for Instance-Based Arabic Language Authorship Attribution
by: Mohammed Al-Sarem, et al.
Published: (2020-01-01) -
Towards Authorship Attribution in Arabic Short-Microblog Text
by: Kamal Mansour Jambi, et al.
Published: (2021-01-01) -
The Effect of Code Obfuscation on Authorship Attribution of Binary Computer Files
by: Hendrikse, Steven
Published: (2017)