Comparison of Adaptive Boosting and Bootstrap Aggregating Performance to Improve the Prediction of Bank Telemarketing - Agus Priyanto; Rila Mandala (President University, 2020)
Background: Telemarketing is an effectivemarketing strategy lately, because it allows long-distanceinteraction making it easier for marketing promotionmanagement to market their products. But sometimes withincessant phone calls to clients that are less potential to causeinconvenience, so we need predictions that produce goodprobabilities so that it can be the basis for making decisionsabout how many potential clients can be contacted whichresults in time and costs can be minimized, telephone calls canbe more effective, client stress and intrusion will be reduced.strong.Method: This study will compare the classificationperformance of Bank Marketing datasets from the UCIMachine Learning Repository using data mining with theAdaboost and Bagging ensemble approach, base algorithmusing J48 Weka, and Wrapper subset evaluation featureselection techniques and previously data balancing wasperformed on the dataset, where the expected results can beknown the best ensemble method that produces the bestperformance of both.Results: In the Bagging experiment, the best performanceof Adaboost and J48 with an accuracy rate of 86.6%, Adaboost83.5% and J48 of 85.9%Conclusion: The conclusion obtainedfrom this study that the use of data balancing and featureselection techniques can help improve classificationperformance, Bagging is the best ensemble algorithm from thisstudy, while for Adaboost is not productive for this studybecause the basic algorithm used is a strong learner whereAdaboost has Weaknesses to improve strong basic algorithm.