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Improving Association Rules Accuracy in Noisy Domains Using instance reduction techniques

Computers, Materials & Continua 2022年第8期72卷 3719-3749页

作者： Mousa Al-Akhras Zainab Darwish Samer Atawneh Mohamed Habib College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia King Abdullah II School for Information Technology The University of JordanAmman11942Jordan Faculty of Engineering Port Said UniversityPort Said42523Egypt

Association rules’learning is a machine learning method used in finding underlying associations in large *** intentionally or unintentionally present,noise in training instances causes overfitting while building the classifier and negatively impacts classification *** paper uses instance reduction techniques for the datasets before mining the association rules and building the *** reduction techniques were originally developed to reduce memory requirements in instance-based *** paper utilizes them to remove noise from the dataset before training the association rules *** experiments were conducted to assess the accuracy of association rules with different instance reduction techniques,namely:Decrementalreduction Optimization Procedure(DROP)3,DROP5,ALL K-Nearest Neighbors(ALLKNN),Edited Nearest Neighbor(ENN),and Repeated Edited Nearest Neighbor(RENN)in different noise *** show that instance reduction techniques substantially improved the average classification accuracy on three different noise levels:0%,5%,and 10%.The RENN algorithm achieved the highest levels of accuracy with a significant improvement on seven out of eight used datasets from the University of California Irvine(UCI)machine learning *** improvements were more apparent in the 5%and the 10%noise *** RENN was applied,the average classification accuracy for the eight datasets in the zero-noise test enhanced from 70.47%to 76.65%compared to the original *** average accuracy was improved from 66.08%to 77.47%for the 5%-noise case and from 59.89%to 77.59%in the 10%-noise *** confidence was also reported in building the association rules when RENN was *** above results indicate that RENN is a good solution in removing noise and avoiding overfitting during the construction of the association rules classifier,especially in noisy domains.

关键词： Association rules classification instance reduction techniques classification overfitting noise data cleansing

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