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2004

Class Noise vs. Attribute Noise: A Quantitative Study

10 years 2 months ago
Class Noise vs. Attribute Noise: A Quantitative Study
Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on the data. Noise can reduce system performance in terms of classification accuracy, time in building a classifier and the size of the classifier. Accordingly, most existing learning algorithms have integrated various approaches to enhance their learning abilities from noisy environments, but the existence of noise can still introduce serious negative impacts. A more reasonable solution might be to employ some preprocessing mechanisms to handle noisy instances before a learner is formed. Unfortunately, rare research has been conducted to systematically explore the impact of noise, especially from the noise handling point of view. This has made various noise processing techniques less significant, specifically when dealing with noise that is introduced in attributes. In this paper, we present a systematic evalu...
Xingquan Zhu, Xindong Wu
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Where AIR
Authors Xingquan Zhu, Xindong Wu
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