Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea
| Autor: | Doreen Ying Ying Sim, Chee Siong Teh, Ahmad Izuanuddin Ismail |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Association rule learning
010308 nuclear & particles physics Computer science business.industry 0211 other engineering and technologies InformationSystems_DATABASEMANAGEMENT 021107 urban & regional planning 02 engineering and technology General Medicine Machine learning computer.software_genre medicine.disease 01 natural sciences Obstructive sleep apnea 0103 physical sciences medicine Pruning (decision trees) Artificial intelligence business computer |
| Zdroj: | Applied Mechanics and Materials. 892:210-218 |
| ISSN: | 1662-7482 |
| DOI: | 10.4028/www.scientific.net/amm.892.210 |
| Popis: | Boosted Association-Ruled Pruned Decision Tree (ARP-DT), the improved version of the Boosted Decision Tree algorithm, was developed by using association-ruled pre-and post-pruning techniques with referring to the pushed minimum support and minimum confidence constraints as well as the association rules applied. The novelty of the Association-Ruled pruning techniques applied mainly embark on the pre-pruning techniques through researching on the maximum number of decision tree splitting, as well as the post-pruning techniques involving subtree replacement and subtree raising. The applied association rules (ARs) augment the mining of frequent itemset (s) or interesting itemset (s) such that appropriate pre-pruning or subtree pruning techniques can be applied before AdaBoost ensemble is implemented. The ARs applied involve the Adaptive Apriori (AA) augmented rule definitions and theorem as stated in this research focuses on the characteristics of the datasets accessed so as to streamline the rule-driven pruning techniques on the Boosting algorithms developed for predicting Obstructive Sleep Apnea (OSA). There is a significant improvement in the prediction accuracies when comparing the classical boosting algorithms and Boosted ARP-DT being applied to the OSA datasets and those online databases from University of California Irvine (UCI) data repositories. |
| Databáze: | OpenAIRE |
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