A comprehensive empirical comparison of modern supervised classification and feature selection methods for text categorization-Reference-Cited by-同舟云学术

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

Published:2014-03-01 Issue:10 Volume:65 Page:1964-1987
ISSN:2330-1635
Container-title:Journal of the Association for Information Science and Technology
language:en
Short-container-title:J Assn Inf Sci Tec

Author:

Aphinyanaphongs Yindalon¹²,Fu Lawrence D.¹²,Li Zhiguo¹,Peskin Eric R.¹,Efstathiadis Efstratios¹,Aliferis Constantin F.¹³⁴,Statnikov Alexander¹²

Affiliation:

1. Center for Health Informatics and Bioinformatics; New York University Langone Medical Center; 227 East 30th Street New York NY 10016

2. Department of Medicine; New York University School of Medicine; 550 First Avenue New York NY 10016

3. Department of Pathology; New York University School of Medicine; 550 First Avenue New York NY 10016

4. Department of Biostatistics; Vanderbilt University; 1211 Medical Center Drive Nashville TN 37232

Publisher

Wiley

Subject

Library and Information Sciences,Information Systems and Management,Computer Networks and Communications,Information Systems

Reference59 articles.

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4. Aphinyanaphongs , Y. Aliferis , C.F. 2006 Prospective validation of text categorization models for identifying high-quality content-specific articles in MEDLINE Proceedings of the Annual American Medical Informatics Association (AMIA) 2006 Symposium 6 10 Bethesda, MD American Medical Informatics Association (AMIA)

5. Text categorization models for high-quality article retrieval in internal medicine;Aphinyanaphongs;Journal of the American Medical Informatics Association,2005

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