논문 상세보기

Support Vector Machine을 이용한 고객이탈 예측모형에 관한 연구 KCI 등재

A Study on Customer Segmentation Prediction Model using Support Vector Machine

  • 언어KOR
  • URLhttps://db.koreascholar.com/Article/Detail/245244
구독 기관 인증 시 무료 이용이 가능합니다. 4,300원
대한안전경영과학회지 (Journal of Korea Safety Management & Science)
대한안전경영과학회 (Korea Safety Management & Science)
초록

Customer segmentation prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. However, ANN approaches have suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning technique, support vector machines (SVM), to the customer segmentation prediction problem in an attempt to provide a model with better explanatory power. To evaluate the prediction accuracy of SVM, we compare its performance with logistic regression analysis and ANN. The experiment results with real data of insurance company show that SVM superiors to them.

저자
  • 서광규 | Seo, Kwang Kyu