TY - JOUR
T1 - A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization
AU - Aphinyanaphongs, Yindalon
AU - Fu, Lawrence D.
AU - Li, Zhiguo
AU - Peskin, Eric R.
AU - Efstathiadis, Efstratios
AU - Aliferis, Constantin F.
AU - Statnikov, Alexander
N1 - Publisher Copyright:
© 2014 ASIS&T.
PY - 2014/10/1
Y1 - 2014/10/1
N2 - An important aspect to performing text categorization is selecting appropriate supervised classification and feature selection methods. A comprehensive benchmark is needed to inform best practices in this broad application field. Previous benchmarks have evaluated performance for a few supervised classification and feature selection methods and limited ways to optimize them. The present work updates prior benchmarks by increasing the number of classifiers and feature selection methods order of magnitude, including adding recently developed, state-of-the-art methods. Specifically, this study used 229 text categorization data sets/tasks, and evaluated 28 classification methods (both well-established and proprietary/commercial) and 19 feature selection methods according to 4 classification performance metrics. We report several key findings that will be helpful in establishing best methodological practices for text categorization.
AB - An important aspect to performing text categorization is selecting appropriate supervised classification and feature selection methods. A comprehensive benchmark is needed to inform best practices in this broad application field. Previous benchmarks have evaluated performance for a few supervised classification and feature selection methods and limited ways to optimize them. The present work updates prior benchmarks by increasing the number of classifiers and feature selection methods order of magnitude, including adding recently developed, state-of-the-art methods. Specifically, this study used 229 text categorization data sets/tasks, and evaluated 28 classification methods (both well-established and proprietary/commercial) and 19 feature selection methods according to 4 classification performance metrics. We report several key findings that will be helpful in establishing best methodological practices for text categorization.
KW - information retrieval
KW - machine learning
KW - text processing
UR - http://www.scopus.com/inward/record.url?scp=84925450131&partnerID=8YFLogxK
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U2 - 10.1002/asi.23110
DO - 10.1002/asi.23110
M3 - Article
AN - SCOPUS:84925450131
SN - 2330-1635
VL - 65
SP - 1964
EP - 1987
JO - Journal of the Association for Information Science and Technology
JF - Journal of the Association for Information Science and Technology
IS - 10
ER -