Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea
An improved Boosting algorithm, named as Boosted PARM-DT, was developed by pre-pruning techniques and Associative Rule Mining (ARM) on decision trees built from the clinical datasets** collected for Obstructive Sleep Apnea (OSA). The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed b...
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| Format: | Article |
| Language: | English |
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American Scientific Publishers
2017
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| Online Access: | http://ir.unimas.my/id/eprint/18814/ http://ir.unimas.my/id/eprint/18814/1/Improved%20Boosting%20-%20Copy.pdf |
| _version_ | 1848838584675598336 |
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| author | Doreen Ying Ying, Sim Chee Siong, Teh Ahmad Izuanuddin, Ismail |
| author_facet | Doreen Ying Ying, Sim Chee Siong, Teh Ahmad Izuanuddin, Ismail |
| author_sort | Doreen Ying Ying, Sim |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | An improved Boosting algorithm, named as Boosted PARM-DT, was developed by pre-pruning techniques and Associative Rule Mining (ARM) on decision trees built from the clinical datasets** collected for Obstructive Sleep Apnea (OSA). The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed by adopting pre-pruning techniques on tree depth, minimum leaf and/or parent node size observations and maximum number of tree splits, based on Apriori and/or Adaptive Apriori (AA) frameworks, is boosted to achieve better predictive accuracies. The improved algorithms were implemented in OSA dataset and UCI online databases for comparisons. Better predictive accuracies were achieved in all the applied datasets/databases when comparing the classical algorithm, i.e. Boosted DT, with the improved one, i.e. Boosted PARM-DT. |
| first_indexed | 2025-11-15T06:57:53Z |
| format | Article |
| id | unimas-18814 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:57:53Z |
| publishDate | 2017 |
| publisher | American Scientific Publishers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-188142022-06-08T08:50:54Z http://ir.unimas.my/id/eprint/18814/ Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea Doreen Ying Ying, Sim Chee Siong, Teh Ahmad Izuanuddin, Ismail QA Mathematics RA Public aspects of medicine An improved Boosting algorithm, named as Boosted PARM-DT, was developed by pre-pruning techniques and Associative Rule Mining (ARM) on decision trees built from the clinical datasets** collected for Obstructive Sleep Apnea (OSA). The Pruned-Associative-Rule-Mined Decision Trees (PARM-DT) developed by adopting pre-pruning techniques on tree depth, minimum leaf and/or parent node size observations and maximum number of tree splits, based on Apriori and/or Adaptive Apriori (AA) frameworks, is boosted to achieve better predictive accuracies. The improved algorithms were implemented in OSA dataset and UCI online databases for comparisons. Better predictive accuracies were achieved in all the applied datasets/databases when comparing the classical algorithm, i.e. Boosted DT, with the improved one, i.e. Boosted PARM-DT. American Scientific Publishers 2017 Article PeerReviewed text en http://ir.unimas.my/id/eprint/18814/1/Improved%20Boosting%20-%20Copy.pdf Doreen Ying Ying, Sim and Chee Siong, Teh and Ahmad Izuanuddin, Ismail (2017) Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea. Advance Science Letters, 23 (11). pp. 11593-11598. ISSN 1936-6612 http://www.aspbs.com/science.htm |
| spellingShingle | QA Mathematics RA Public aspects of medicine Doreen Ying Ying, Sim Chee Siong, Teh Ahmad Izuanuddin, Ismail Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea |
| title | Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea |
| title_full | Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea |
| title_fullStr | Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea |
| title_full_unstemmed | Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea |
| title_short | Improved Boosting Algorithms by Pre-Pruning and Associative Rule Mining on Decision Trees for predicting Obstructive Sleep Apnea |
| title_sort | improved boosting algorithms by pre-pruning and associative rule mining on decision trees for predicting obstructive sleep apnea |
| topic | QA Mathematics RA Public aspects of medicine |
| url | http://ir.unimas.my/id/eprint/18814/ http://ir.unimas.my/id/eprint/18814/ http://ir.unimas.my/id/eprint/18814/1/Improved%20Boosting%20-%20Copy.pdf |