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2004
Springer

Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources

9 years 6 months ago
Dealing with Predictive-but-Unpredictable Attributes in Noisy Data Sources
Attribute noise can affect classification learning. Previous work in handling attribute noise has focused on those predictable attributes that can be predicted by the class and other attributes. However, attributes can often be predictive but unpredictable. Being predictive, they are essential to classification learning and it is important to handle their noise. Being unpredictable, they require strategies different from those of predictable attributes. This paper presents a study on identifying, cleansing and measuring noise for predictive-but-unpredictable attributes. New strategies are accordingly proposed. Both theoretical analysis and empirical evidence suggest that these strategies are more effective and more efficient than previous alternatives.
Ying Yang, Xindong Wu, Xingquan Zhu
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where PKDD
Authors Ying Yang, Xindong Wu, Xingquan Zhu
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