Domain knowledge integration in data mining using decision tables: case studies in churn prediction

By Elen Lima, C. Mues, Bart Baesens. 2009

86 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Organizational Behavior and Human Resource Management

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Citations in the corpus

Interpretable Two-level Boolean Rule Learning for Classification (2015, background): “In applications where machine learning is used to aid human decision-making, it is recognized that interpretabili ty of models is an important objective for establishing trust, adoption and safety and for offering the possibility of auditing and debugging ( Freitas, 2014; Varshney, 2016; Lima et al.”
Differential Privacy in Telco Big Data Platform (2015, methodology): “For example, DTs have been used for churn analysis [32], domain knowledge integration [19], voltage reduction [1], and so on.”
A Bayesian Approach to Learning Scoring Systems (2015, background): “…(Freitas, Wieser, and Apweiler, 2010), credit scoring (Siddiqi, 2005; Martens et al., 2007; Martens, Baesens, and Van Gestel, 2009), churn analysis (Lima, Mues, and Baesens, 2009; Verbeke et al., 2011) to crime prediction (Ridgeway, 2013; Andrade, 2009), often a model can only be defensible if it…”
Building comprehensible customer churn prediction models with advanced rule induction techniques (2011, background): “As can be seen from the table, only for a few features an explicit relation is presumed, in accordance with Lima et al. (2009).”
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).”
Comprehensible classification models: a position paper (2014, methodology): “Decision tables are a tabular knowledge representation where the state of a set of conditions jointly determines one or more outcomes [47].”