[논문]설명 가능한 정기예금 가입 여부 예측을 위한 앙상블 학습 기반 분류 모델들의 비교 분석

신지안; 문지훈; 노승민

doi:10.7838/jsebs.2021.26.3.097

설명 가능한 정기예금 가입 여부 예측을 위한 앙상블 학습 기반 분류 모델들의 비교 분석
A Comparative Analysis of Ensemble Learning-Based Classification Models for Explainable Term Deposit Subscription Forecasting 원문보기

한국전자거래학회지 = The Journal of Society for e-Business Studies, v.26 no.3, 2021년, pp.97 - 117

신지안 (Department of Security Convergence Science, Chung-Ang University) , 문지훈 (Chung-Ang University) , 노승민 (Department of Industrial Security, Chung-Ang University)

초록
AI-Helper

정기예금 가입 여부 예측은 은행의 대표적인 금융 마케팅 중 하나로, 은행은 다양한 고객 정보를 활용하여 예측 모델을 구성할 수 있다. 정기예금 가입 여부의 분류 정확도를 향상하기 위해, 많은 연구에서 기계학습 기법들을 이용하여 분류 모델들을 개발하였다. 하지만, 이러한 모델들이 만족스러운 성능을 보일지라도 모델의 의사결정 과정에 대한 근거가 적절하게 설명되지 않는다면 산업에서 활용하기가 쉽지 않다. 이러한 문제점을 해결하기 위해, 본 논문은 설명 가능한 정기예금 가입 여부 예측 기법을 제안한다. 먼저, 테이블 형식에서 우수한 성능을 도출하는 의사결정 나무 기반 앙상블 학습 기법인 랜덤 포레스트, GBM, XGBoost, LightGBM을 이용하여 분류 모델들을 개발하고, 10겹 교차검증을 통해 모델들의 분류 성능을 심층 분석한다. 다음으로, 가장 우수한 성능을 도출하는 모델에 설명 가능한 인공지능 기법인 SHAP을 적용하여 고객 정보의 영향도와 의사결정 과정 등을 해석할 수 있는 근거를 제공한다. 제안한 기법의 실용성과 타당성을 입증하기 위해, Kaggle에서 제공한 은행 마케팅 데이터 셋을 대상으로 모의실험을 진행하였으며, 데이터 셋 구성에 따라 GBM과 LightGBM 모델에 SHAP을 각기 적용하여 설명 가능한 정기예금 가입 여부를 위한 분석 및 시각화를 수행하였다.

Abstract ▼ AI-Helper

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

주제어

표/그림 (18)

표 Summary of Previous Works Based on XAI with SHAP
그림 System Architecture
표 List of Independent and Dependent Variables and Their Data Type
표 Advantage and Disadvantage of Tree-based Ensemble Learning Methods
그림 Flowchart of Bagging Process
그림 Illustration Demonstrating the Difference between Leaf-Wise Growth (above) and Level-Wise (below)
그림 Example of 10-fold Cross-Validation
표 Confusion Matrix
표 Guidelines of Landis and Koch
표 Selected Hyperparameters for Each Tree-based Ensemble Learning Model
표 Performance Comparison (the values in the bold font indicate the best values for respective metrics)
그림 Summary Plot of Selected SHAP Values
표 Performance Comparison on the Dataset Excluding Duration (the values in the bold font indicate the best values for respective metrics)
그림 SHAP Dependent Plot Shows that Effect of Duration on Model Output with Respect to Balance
그림 SHAP Dependent Plot Shows that Effect of Age on Model Output with Respect to Housing
그림 Summary Plot of Selected SHAP Values on the Dataset Excluding Duration
그림 SHAP Decision Plot
그림 SHAP Decision Plot on the Dataset Excluding Duration

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동의어: Packet Collision Rate

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