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Published Articles >> Table of Contents >> Abstract
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 1
p. 471
A New Reparation Method for Incomplete Data in the Context of Supervised Learning
Matteo Magnani, University of Bologna, Italy
Danilo Montesi, University of Camerino, Italy
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ITCC.2004.1286501
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| Abstract |
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Real-world data is often incomplete. There exist many
statistical methods to deal with missing items. However,
they assume data distributions which are difficult to justify
in the context of supervised learning. In this paper we propose
a new method of repairing incomplete data. This technique
is a variation of a general strategy, here called local
imputation. It repairs incomplete records, only when this is
reasonable. It is able to identify wrong tuples. It is more
general than other similar methods, because of a parametric
similarity function. Finally, it also works with noisy data
sets.
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 Additional Information
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Citation:
Matteo Magnani, Danilo Montesi,
"A New Reparation Method for Incomplete Data in the Context of Supervised Learning,"
itcc,
p. 471,
International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 1,
2004
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