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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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/258054 |
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| _version_ | 1832551041645674496 |
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| author | Hyontai Sug |
| author_facet | Hyontai Sug |
| author_sort | Hyontai Sug |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-44f438d15144405880a1c5d1852cc8a4 |
| institution | Kabale University |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-44f438d15144405880a1c5d1852cc8a42025-02-03T06:05:05ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/258054258054Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient InformationHyontai Sug0Division of Computer and Information Engineering, Dongseo University, Busan 617-716, Republic of KoreaRandom 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.http://dx.doi.org/10.1155/2012/258054 |
| spellingShingle | Hyontai Sug Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information Journal of Applied Mathematics |
| title | Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information |
| title_full | Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information |
| title_fullStr | Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information |
| title_full_unstemmed | Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information |
| title_short | Applying Randomness Effectively Based on Random Forests for Classification Task of Datasets of Insufficient Information |
| title_sort | applying randomness effectively based on random forests for classification task of datasets of insufficient information |
| url | http://dx.doi.org/10.1155/2012/258054 |
| work_keys_str_mv | AT hyontaisug applyingrandomnesseffectivelybasedonrandomforestsforclassificationtaskofdatasetsofinsufficientinformation |