A Case Study in Credit Fraud Detection With SMOTE and XGBoost
Cuizhu Meng, Zhou Li, Bisong Liu
Abstract
Abstract Credit fraud observations are minority in the sample set, variables tend to be seriously unbalanced, and the prediction results tend to be biased towards more observed classes. Common resolution usually constructs 1:1 data, either cutting off part of more classes (undersampling) or reducing classes for bootstrap sampling (oversampling). XGBoost is an efficient system implementation of Gradient Boosting, and also GB algorithm based on CART. Based on the real online transaction data of an Internet financial institution, this paper studies the performance of XGBoost algorithm on the original data set, the undersampling and SMOTE data sets respectively.
Topics & Concepts
UndersamplingOversamplingComputer scienceDatabase transactionData miningBoosting (machine learning)Data setGradient boostingTransaction dataThe InternetSupport vector machineMachine learningArtificial intelligenceRandom forestDatabaseBandwidth (computing)World Wide WebComputer networkImbalanced Data Classification TechniquesFinancial Distress and Bankruptcy PredictionRough Sets and Fuzzy Logic