TRANSACTION FRAUD PREDICTION MODEL BASED ON FEATURE SIMILARITY DOWN-SAMPLING
In the scenario of transaction fraud evaluation,the proportion of positive and negative samples is extremely different,so it is necessary to sample the samples to solve the sample imbalance.Due to the loss of sample information in the traditional sampling process,the accuracy of model prediction is not very high.Aimed at this kind of situation,a model construction method based on feature similarity down-sampling is proposed.This method mainly included three parts.(1)According to the sample data,an effective feature set related to fraud was constructed.(2)By introducing the sample difference function,as much sample information as possible was retained when down-sampling.(3)Multiple classifiers were fused to output the fraud probability.This method was compared with other common sampling methods.Experimental results show that this method has better evaluation results.
国家科技期刊平台
NSTL
万方数据
维普
