Mining the customer credit using classification and regression tree and multivariate adaptive regression splines

By Tian-Shyug Lee, Chih-Chou Chiu, Yu-Chao Chou, Chi-Jie Lu. Computational Statistics & Data Analysis, 2006

392 citations recorded by the source corpus.

Subfields: Accounting, Control and Systems Engineering, Marketing

View Paper: https://www.semanticscholar.org/paper/342bf0217ee68f9a144e05933e686c152f4c63ae

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Yufei Xia
2. Gianluca Bontempi
3. Eric Wai Ting Ngai
4. Chuanzhe Liu
5. Anderson Ara
6. Andrea Dal Pozzolo
7. Francisco Louzada
8. Yuying Li

In Control and Systems Engineering

1. Gianluca Bontempi
2. Eric Wai Ting Ngai
3. Andrea Dal Pozzolo
4. Francisco Louzada
5. Yuying Li

In Marketing

1. John Hadden
2. Dorothy C. K. Chau
3. Ashutosh Tiwari
4. Eric Wai Ting Ngai
5. Xiu Li
6. Dymitr Ruta
7. Rajkumar Roy

Citations in the corpus

• Computer assisted customer churn management: State-of-the-art and future trends (2007, background): “It is documented that on a standard PC, it took roughly 20 hours to analyse a dataset of 8000, which is a major drawback of the technology (Lee et al., 2004).”
• Classification methods applied to credit scoring: A systematic review and overall comparison (2016, background): “Other important comparisons may be seen in Adams et al. (2001); Hoffmann et al. (2002); Ong et al. (2005); Baesens et al. (2005); Wang et al. (2005); Lee et al. (2006); Huang et al. (2006b); Xiao et al. (2006); Van Gestel et al. (2007); Martens et al. (2007); Hu and Ansell (2007); Tsai (2008);…”
• Application of data mining techniques in customer relationship management: A literature review and classification (2009, methodology): “…network, K-nearest neighbor and decision tree Datta et al. (2000) Classification and regression tree and multivariate adaptive regression splines Lee et al. (2006) Logistic regression and neural network Kim (2006) Logistic regression, neural network and random forest Buckinx and Poel (2005)…”
• A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring (2017, methodology): “, 2007; Min & Lee, 2005), and decision tree (DT) (Lee et al., 2006; Nie et al., 2011).”
• Adaptive Machine Learning for Credit Card Fraud Detection (2015, background): “Decision trees have found many applications in fraud detection, for both credit card fraud detection [126] and credit risk scoring [195].”
• Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015, neutral): “10 X X (Lee et al., 2006) 1 8,000 9 5 X X (Li et al.”