Class Noise vs. Attribute Noise: A Quantitative Study

Author:

Zhu Xingquan,Wu Xindong

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference52 articles.

1. Allison, P.D. (2002). Missing Data. Thousand Oaks, CA:Sage.

2. Bansal, N., Chawla, S. & Gupta, A. (2000). Error Correction in Noisy Datasets Using Graph Mincuts.Project Report, Carnegie Mellon University, http://www.cs.cmu. edu/15781/web/proj/chawla.ps.

3. Batista, G. & Monard, M.C. (2003). An Analysis of Four Missing Data Treatment Methods for Supervised Learning. Applied Artificial Intelligence 17:519?533.

4. Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases, http://www.ics.uci.edu/mlearn/MLRepository.html

5. Brodley, C.E. & Friedl, M.A. (1996). Identifying and Eliminating Mislabeled Training Instances. Proc. of 13th National Conf. on Artificial Intelligence, 799?805.

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