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Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information
Author
Source
Journal of Applied Mathematics
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-11-07
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
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 is one of such datasets.
Hence, in this paper, some best classification accuracy based on clever application of random forests to predict the occurrence of cylinder bands in rotogravure printing is investigated.
Since random forests could generate good results with an appropriate combination of parameters like the number of randomly selected attributes for each split and the number of trees in the forests, an effective data mining procedure considering the property of the target dataset by way of trial random forests is investigated.
The effectiveness of the suggested procedure is shown by experiments with very good results.
American Psychological Association (APA)
Sug, Hyontai. 2012. Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information. Journal of Applied Mathematics،Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-993083
Modern Language Association (MLA)
Sug, Hyontai. Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information. Journal of Applied Mathematics No. 2012 (2012), pp.1-13.
https://search.emarefa.net/detail/BIM-993083
American Medical Association (AMA)
Sug, Hyontai. Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information. Journal of Applied Mathematics. 2012. Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-993083
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-993083