A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization

Yindalon Aphinyanaphongs, Lawrence D. Fu, Zhiguo Li, Eric R. Peskin, Efstratios Efstathiadis, Constantin F. Aliferis, Alexander Statnikov

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1964-1987
Number of pages24
JournalJournal of the Association for Information Science and Technology
Volume65
Issue number10
DOIs
StatePublished - Oct 1 2014

Keywords

  • information retrieval
  • machine learning
  • text processing

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