Improving Association Rules Accuracy in Noisy Domains Using Instance Reduction Techniques

作     者：Mousa Al-Akhras Zainab Darwish Samer Atawneh Mohamed Habib 

作者机构：College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia King Abdullah II School for Information TechnologyThe University of JordanAmman11942Jordan Faculty of EngineeringPort Said UniversityPort Said42523Egypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第8期

页      面：3719-3749页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 08[工学] 071102[理学-系统分析与集成] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0711[理学-系统科学] 0805[工学-材料科学与工程（可授工学、理学学位）] 081101[工学-控制理论与控制工程] 0701[理学-数学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 081103[工学-系统工程] 

基　　金：The APC was funded by the Deanship of Scientific Research Saudi Electronic University 

主　　题：Association rules classification instance reduction techniques classification overfitting noise data cleansing 

摘      要：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.