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Title: Impacts of noise on machine learning
Authors: Qiao, Kaimu
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2010
Abstract: Noise in real-world data is quite common and may impact the interpretations of the data, classifiers constructed from the data and classification decisions made based on the data. It can decrease the classifier's performance in many aspects such as classification accuracy, time in building a classifier and so on. Handling noise has become a key issue in machine learning and data mining. Many learning algorithms have been modified to enhance their performance in the environment of noise, like robust learners. However, when the noise level goes high, the performance of these algorithms may not be as good as expected. Thus, a better way is to preprocess the data before a classifier is constructed. Conventional mechanisms include noise filter and data polishing.
Description: 96 p.
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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