Ensemble classifier and resampling for imbalanced multiclass learning
An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in orde...
| Hlavní autoři: | , , |
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| Médium: | Conference or Workshop Item |
| Jazyk: | English |
| Vydáno: |
2015
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| Témata: | |
| On-line přístup: | https://repo.uum.edu.my/id/eprint/15678/1/PID047.pdf |
| _version_ | 1825803576126996480 |
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| author | Sainin, Mohd Shamrie Ahmad, Faudziah Alfred, Rayner |
| author_facet | Sainin, Mohd Shamrie Ahmad, Faudziah Alfred, Rayner |
| author_sort | Sainin, Mohd Shamrie |
| collection | UUM |
| description | An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging
and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifier |
| first_indexed | 2024-07-04T05:59:22Z |
| format | Conference or Workshop Item |
| id | uum-15678 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T05:59:22Z |
| publishDate | 2015 |
| record_format | eprints |
| spelling | uum-156782016-04-27T08:46:44Z https://repo.uum.edu.my/id/eprint/15678/ Ensemble classifier and resampling for imbalanced multiclass learning Sainin, Mohd Shamrie Ahmad, Faudziah Alfred, Rayner QA75 Electronic computers. Computer science An ensemble classifier called DECIML has previously reported that the classifier is able to perform on benchmark data compared to several single classifiers and ensemble classifiers such as AdaBoost, Bagging and Random Forest.The implementation of the ensemble using sampling was carried out in order to investigate if there are any improvements in the classification performances of the DECIML.Random sampling with replacement (SWR) method is applied to minority class in the imbalanced multiclass data. Results show that the SWR is able to increase the average performance of the ensemble classifier 2015 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/15678/1/PID047.pdf Sainin, Mohd Shamrie and Ahmad, Faudziah and Alfred, Rayner (2015) Ensemble classifier and resampling for imbalanced multiclass learning. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey. http://www.icoci.cms.net.my/proceedings/2015/TOC.html |
| spellingShingle | QA75 Electronic computers. Computer science Sainin, Mohd Shamrie Ahmad, Faudziah Alfred, Rayner Ensemble classifier and resampling for imbalanced multiclass learning |
| title | Ensemble classifier and resampling for imbalanced multiclass learning |
| title_full | Ensemble classifier and resampling for imbalanced multiclass learning |
| title_fullStr | Ensemble classifier and resampling for imbalanced multiclass learning |
| title_full_unstemmed | Ensemble classifier and resampling for imbalanced multiclass learning |
| title_short | Ensemble classifier and resampling for imbalanced multiclass learning |
| title_sort | ensemble classifier and resampling for imbalanced multiclass learning |
| topic | QA75 Electronic computers. Computer science |
| url | https://repo.uum.edu.my/id/eprint/15678/1/PID047.pdf |
| work_keys_str_mv | AT saininmohdshamrie ensembleclassifierandresamplingforimbalancedmulticlasslearning AT ahmadfaudziah ensembleclassifierandresamplingforimbalancedmulticlasslearning AT alfredrayner ensembleclassifierandresamplingforimbalancedmulticlasslearning |