Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information

Random forests are known to be good for data mining of classification tasks, because random forests are robust for datasets having insufficient information possibly with some errors. But applying random forests blindly may not produce good results, and a dataset in the domain of rotogravure printing...

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Bibliographic Details
Main Author: Hyontai Sug
Format: Article
Language:English
Published: Wiley 2012-01-01
Series:Journal of Applied Mathematics
Online Access:http://dx.doi.org/10.1155/2012/258054
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