Pushing Constraints by Rule-Driven Pruning Techniques in Non-Uniform Minimum Support for Predicting Obstructive Sleep Apnea

Abstract:

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.

• Periodical: Applied Mechanics and Materials (Volume 892)
• Pages: 210-218
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.892.210
• Online since: June 2019
• Authors: Doreen Ying Ying Sim, Chee Siong Teh, Ahmad Izuanuddin Ismail
• Keywords: Adaptive Apriori, Association Rules, Boosted Association-Ruled Pruned Decision Tree, Frequent Itemset (s), Minimum Confidence Constraints, Pushed Minimum Support, Subtree Raising, Subtree Replacement

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