Data-Driven Decision-Making for Bank Target Marketing Using Supervised Learning Classifiers on Imbalanced Big Data  

作　　者：Fahim Nasir Abdulghani Ali Ahmed Mehmet Sabir Kiraz Iryna Yevseyeva Mubarak Saif 

机构地区：[1]School of Computer Science and Informatics,De Montfort University,Leicester,LE19BH,UK [2]Faculty of Computer Science and Information Technology,Universiti Tun Hussein Onn Malaysia,Johor,86400,Malaysia

出　　处：《Computers, Materials & Continua》2024年第10期1703-1728,共26页计算机、材料和连续体（英文）

基　　金：support from the Cyber Technology Institute(CTI)at the School of Computer Science and Informatics,De Montfort University,United Kingdom,along with financial assistance from Universiti Tun Hussein Onn Malaysia and the UTHM Publisher’s office through publication fund E15216.

摘　　要：Integrating machine learning and data mining is crucial for processing big data and extracting valuable insights to enhance decision-making.However,imbalanced target variables within big data present technical challenges that hinder the performance of supervised learning classifiers on key evaluation metrics,limiting their overall effectiveness.This study presents a comprehensive review of both common and recently developed Supervised Learning Classifiers(SLCs)and evaluates their performance in data-driven decision-making.The evaluation uses various metrics,with a particular focus on the Harmonic Mean Score(F-1 score)on an imbalanced real-world bank target marketing dataset.The findings indicate that grid-search random forest and random-search random forest excel in Precision and area under the curve,while Extreme Gradient Boosting(XGBoost)outperforms other traditional classifiers in terms of F-1 score.Employing oversampling methods to address the imbalanced data shows significant performance improvement in XGBoost,delivering superior results across all metrics,particularly when using the SMOTE variant known as the BorderlineSMOTE2 technique.The study concludes several key factors for effectively addressing the challenges of supervised learning with imbalanced datasets.These factors include the importance of selecting appropriate datasets for training and testing,choosing the right classifiers,employing effective techniques for processing and handling imbalanced datasets,and identifying suitable metrics for performance evaluation.Additionally,factors also entail the utilisation of effective exploratory data analysis in conjunction with visualisation techniques to yield insights conducive to data-driven decision-making.

关 键 词：Big data machine learning data mining data visualization label encoding imbalanced dataset sampling techniques 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]