CHAMP: A Prototype for Automated Cellular Churn Prediction

Data Mining and Knowledge DiscoveryPublished 1 June 1999

Brij Masand, Piew Datta, D.R. Mani, Bin Li

DOI10.1023/a:1009873905876

Source URL

Citations30

SJR quartileQ1

SJR score1.02

SNIP1.88

TL;DR

CHAMP (CHurn Analysis, Modeling, and Prediction), an automated system for modeling cellular customer behavior on a large scale, which is capable of developing customized monthly models and churn scores for over one hundred GTE cellular phone markets totaling over 5 million customers.

Abstract

We describe CHAMP (CHurn Analysis, Modeling, and Prediction), an automated system for modeling cellular customer behavior on a large scale. Using historical data from GTE's data warehouse for cellular phone customers, every month CHAMP identifies churn factors for several geographic regions and updates models to generate churn scores predicting who is likely to churn within the near future. CHAMP is capable of developing customized monthly models and churn scores for over one hundred GTE cellular phone markets totaling 5 million customers. © 1999 Kluwer Academic Publishers.

Keywords

Computer Science

BiometricsClassification and Regression Trees.

23,841 Citations1984Alexander Gordon, Leo Breiman +3 more

C4.5: Programs for Machine Learning

23,665 Citations1992J. R. Quinlan

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.

Learning Internal Representations by Error Propagation

16,235 Citations1985David E. Rumelhart, Geoffrey E. Hinton +1 more

Elsevier eBooksLearning Internal Representations by Error Propagation

3,013 Citations1988David E. Rumelhart, Geoffrey E. Hinton +1 more

This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.

Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)The Cascade-Correlation learning architecture

2,401 Citations2018Scott E. Fahlman, Christian Lebière

The Cascade-Correlation architecture has several advantages over existing algorithms: it learns very quickly, the network determines its own size and topology, it retains the structures it has built even if the training set changes, and it requires no back-propagation of error signals through the connections of the network.

View PDF

Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.

2,171 Citations1997Miroslav Kubát, Stan Matwin

Elsevier eBooksReducing Misclassification Costs

343 Citations1994Michael J. Pazzani, Christopher J. Merz +4 more

Algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples are explored and the Reduced Cost Ordering algorithm, a new method for creating a decision list, is described and compared to a variety of inductive learning approaches.

Decoupled extended Kalman filter training of feedforward layered networks

233 Citations2002G.V. Puskorius, L.A. Feldkamp

These studies demonstrate that the judicious grouping of weights along with the use of artificial process noise in DEKF result in input-output mapping performance that is comparable to the global extended Kalman algorithm, and is often superior to SBP, while requiring significantly fewer presentations of training data than SBP and less overall training time than either of these procedures.

MLC++: a machine learning library in C++

197 Citations2002Ron Kohavi, George H. John +3 more

The problems M LC++ aims to solve, the design of MLC++, and the current functionality are discussed, including the attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible.

Elsevier eBooksMega induction: a Test Flight

88 Citations1991Jason Catlett

This case study examines the application of Quinlan's C4.5 to the task of diagnosing a subsystem of NASA's Space Shuttle, finding the trees produced to be highly accurate, moderately small, and to contain new knowledge that might otherwise have remained undiscovered.