CenKNN: a scalable and effective text classifier-Reference-Cited by-同舟云学术

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CenKNN: a scalable and effective text classifier

Published:2014-07-03 Issue:3 Volume:29 Page:593-625
ISSN:1384-5810
Container-title:Data Mining and Knowledge Discovery
language:en
Short-container-title:Data Min Knowl Disc

Author:

Pang Guansong,Jin Huidong,Jiang Shengyi

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Link

http://link.springer.com/content/pdf/10.1007/s10618-014-0358-x.pdf

Reference56 articles.

1. Achlioptas D (2003) Database-friendly random projections: Johnson–Lindenstrauss with binary coins. J Comput Syst Sci 66(4):671–687

2. Aggarwal CC, Zhai C (2012) A survey of text classification algorithms. Mining text data. Springer, New York

3. Batista GE, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newslett 6(1):20–29

4. Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM 18(9):509–517

5. Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, pp. 245–250 (2001)

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