A novel evolutionary data mining algorithm with applications to churn prediction

By Wai-Ho Au, Keith C. C. Chan, Xin Yao. IEEE Transactions on Evolutionary Computation, 2003

374 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Marketing

View Paper: https://www.semanticscholar.org/paper/4cbd3754645c60ad0c6f792c503be14b99ecd1db

How researchers see this paper

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

In Artificial Intelligence

1. David Martens
2. Wouter Verbeke
3. Bart Baesens
4. Dorothy C. K. Chau
5. Eric Wai Ting Ngai
6. Weiyun Ying
7. Xiu Li
8. Anupam Joshi

In Information Systems

1. Wouter Verbeke
2. David Martens
3. Bart Baesens
4. Joonyoung Hur
5. Karel Dejaeger
6. Mohand Tahar Kechadi
7. Dorothy C. K. Chau
8. K. Diamantaras

In Marketing

1. David Martens
2. Wouter Verbeke
3. Joonyoung Hur
4. Karel Dejaeger
5. Thorsten Strufe
6. Conor Hayes
7. Mohand Tahar Kechadi
8. Dorothy C. K. Chau

Citations in the corpus

• A comparison of machine learning techniques for customer churn prediction (2015, result): “In the case of the customer churn problem, Au e.a. [31] have shown that neural networks achieve better performance compared to Decision Trees.”
• Churn in Social Networks (2010, background): “Two of them are DMEL (Data Mining by Evolutionary Learning) [2] and CHAMP (CHurn Analysis, Modeling, and Prediction) [44, 14].”
• Customer churn prediction in telecommunications (2012, background): “The details of DMEL techniques can be found in literature (Au et al., 2003).”
• Application of data mining techniques in customer relationship management: A literature review and classification (2009, background): “…(2002), Mozer et al. (2000), Smith et al. (2000) Self-organizing map and decision tree Chu, Isai, and Ho (2007) Data mining by evolutionary learning Au et al. (2003) Support vector machine and latent class model Cheung et al. (2003) Decision tree, naive Bayes, rule based RIPPER, K-nearest network,…”
• New insights into churn prediction in the telecommunication sector: A profit driven data mining approach (2012, methodology): “…regression (Lemmens and Croux, 2006; Neslin et al., 2006; Burez and Van den Poel, 2009), non-parametric statistical models like for instance k-nearest neighbor (Datta et al., 2000), decision trees (Wei and Chiu, 2002; Lima et al., 2009), and neural networks (Au et al., 2003; Hung et al., 2006).”
• Customer churn prediction using improved balanced random forests (2009, methodology): “These problems prevent the above techniques from being applicable to the churn prediction problem (Au et al., 2003).”
• Social ties and their relevance to churn in mobile telecom networks (2008, methodology): “Traditional solutions have used data mining techniques [1,8,24] that create a customer profile from her calling pattern (often described by hundreds of variables), and then predict the probability of churn based on certain attributes of the subscriber.”