Journal ArticleDOI
A comparison of machine learning techniques for customer churn prediction
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TLDR
A comparative study on the most popular machine learning methods applied to the challenging problem of customer churning prediction in the telecommunications industry demonstrates clear superiority of the boosted versions of the models against the plain (non-boosted) versions.About:
This article is published in Simulation Modelling Practice and Theory.The article was published on 2015-06-01. It has received 256 citations till now. The article focuses on the topics: Boosting (machine learning) & AdaBoost.read more
Citations
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Journal ArticleDOI
Customer churn prediction in the telecommunication sector using a rough set approach
TL;DR: This study proposes an intelligent rule-based decision-making technique, based on rough set theory (RST), to extract important decision rules related to customer churn and non-churn, and shows that RST based on GA is the most efficient technique for extracting implicit knowledge in the form of decision rules from the publicly available, benchmark telecom dataset.
Posted Content
Automated Machine Learning: State-of-The-Art and Open Challenges.
TL;DR: A comprehensive survey for the state-of-the-art efforts in tackling the CASH problem is presented and the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment is highlighted.
Journal ArticleDOI
Optimization of laser welding process parameters of stainless steel 316L using FEM, Kriging and NSGA-II
TL;DR: The results of verification experiments indicate that the optimal process parameters are effective and reliable for producing expected welding bead profile.
Journal ArticleDOI
Customer churn prediction system: a machine learning approach
TL;DR: In this article, the authors proposed a methodology consisting of six phases, data preprocessing and feature analysis, feature selection is taken into consideration using gravitational search algorithm, and the data has been split into two parts train and test set in the ratio of 80% and 20% respectively.
Journal ArticleDOI
In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions
Eva Ascarza,Scott A. Neslin,Oded Netzer,Zachery Anderson,Peter S. Fader,Sunil Gupta,Bruce G. S. Hardie,Aurélie Lemmens,Barak Libai,David T. Neal,Foster Provost,Rom Y. Schrift +11 more
TL;DR: In this paper, the authors present an integrated framework for managing retention that leverages emerging opportunities offered by new data sources and new methodologies such as machine learning, highlighting the importance of distinguishing between which customers are at risk and which should be targeted.
References
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Book
C4.5: Programs for Machine Learning
TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Journal ArticleDOI
An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
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A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Proceedings ArticleDOI
A training algorithm for optimal margin classifiers
TL;DR: A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented, applicable to a wide variety of the classification functions, including Perceptrons, polynomials, and Radial Basis Functions.
Journal ArticleDOI
Additive Logistic Regression : A Statistical View of Boosting
TL;DR: This work shows that this seemingly mysterious phenomenon of boosting can be understood in terms of well-known statistical principles, namely additive modeling and maximum likelihood, and develops more direct approximations and shows that they exhibit nearly identical results to boosting.