Earticle

본 연구는 P2P 대부 플랫폼에서 우수 대출자를 예측시 유용한 합성 소수집단 오버샘플링 기법을 제안하고 그 성과를 실증적으로 검증하고자 한다. P2P 대부 관련 우수 대출자를 추정할 때 일어나는 문제점중의 하나는 클래스 간 불균형이 심하여 이를 해결하지 않고서는 우수 대출자 예측이 쉽지 않다는 점이다. 이러한 문제를 해결하기 위하여 본 연구에서는 SMOTE, 즉 합성 소수집단 오버샘플링 기법을 제안하고 LendingClub 데이터셋에 적용하여 성과를 검증하였다. 검증결과 SMOTE 방법은 서포트 벡터머신, k-최근접이웃, 로지스틱 회귀, 랜덤 포레스트, 그리고 딥 뉴럴 네트워크 분류기와 비교하여 통계적으로 우수한 성과를 보였다.

This study aims to identify good borrowers within the context of P2P lending. P2P lending is a growing platform that allows individuals to lend and borrow money from each other. Inherent in any loans is credit risk of borrowers and needs to be considered before any lending. Specifically in the context of P2P lending, traditional models fall short and thus this study aimed to rectify this as well as explore the problem of class imbalances seen within credit risk data sets. This study implemented an over-sampling technique known as Synthetic Minority Over-sampling Technique (SMOTE). To test our approach, we implemented five benchmarking classifiers such as support vector machines, logistic regression, k-nearest neighbor, random forest, and deep neural network. The data sample used was retrieved from the publicly available LendingClub dataset. The proposed SMOTE revealed significantly improved results in comparison with the benchmarking classifiers. These results should help actors engaged within P2P lending to make better informed decisions when selecting potential borrowers eliminating the higher risks present in P2P lending.

Recent advancement in the field of life science data mining has inspired researchers and healthcare professionals to apply this novel technology to obtain descriptive patterns and predictive models from biomedical and healthcare databases. The discovery of hidden biomedical patterns from large clinical database can uncover potential knowledge to support prognosis and diagnosis decision makings. However, clinical application of data mining algorithms has a severe problem of low predictive accuracy rate that hampers their wide usage in the clinical environment. We thus focus our study on the improvement of predictive accuracy of the models created from the data mining algorithms. Our main research interest concerns the problem of learning a classification model from a multiclass data set with low prevalence rate of some minority classes. With such data characteristics, directly applying classification data mining techniques such as decision tree induction, regression analysis, neural networks, or support vector machines yields a suboptimal model in terms of predictive accuracy rate. To remedy the imbalanced class distribution among data instances, we apply random over-sampling and synthetic minority over-sampling (SMOTE) techniques to increase the predictive performance of the learned model. In our preliminary study, we consider specific kinds of primary tumors occurring at the frequency rate less than one percent as rare and minority classes. From the experimental results, the SMOTE technique gave a high specificity model, whereas the random over-sampling produced a high sensitivity classifier. The precision performance of a classification model obtained from the random over-sampling technique is on average much better than the model learned from the original imbalanced data set. We then extend our study by designing the heuristic based method to cope with the abundance of irrelevant feature that causes the decrease in learning time and sometimes lower the accuracy rate. The over-sampling technique and the heuristic-based feature selection are coupled as a data preparation method to deal with imbalanced data sets with many irrelevant features. The experimental results on arrhythmia and communities-and-crime data sets show significant improvement on the predicting accuracy, specificity, and sensitivity of the induced models.