Gene Expression Programming Based Dataset Decoration for Improved Churn Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Misc{Trif:2013:IJERA,

• author = "Silvia Trif and Adrian Visoiu",
• title = "Gene Expression Programming Based Dataset Decoration for Improved Churn Prediction",
• keywords = "genetic algorithms, genetic programming, gene expression programming, business intelligence, churn prediction, classification, data mining",
• abstract = "Mobile network operators rely on business intelligence tools to derive valuable information regarding their subscribers. A key objective is to reduce churn rate among subscribers. The mobile operator needs to know in advance which subscribers are at risk of becoming churners. This problem is solved with classification algorithms having as input data derived from the large volumes of usage details recorded. For certain categories of subscribers, available data is limited to call details records. Using this primary data, a dataset is created to be conveniently used by a classification algorithm. Classification quality using this initial dataset is improved by a proposed method for dataset decoration. Additional attributes are derived from the initial dataset through generation, based on gene expression programming. Classification results obtained using the decorated dataset show that the derived attributes are relevant for the studied problem.",
• annote = "The Pennsylvania State University CiteSeerX Archives",
• bibsource = "OAI-PMH server at citeseerx.ist.psu.edu",
• language = "en",
• oai = "oai:CiteSeerX.psu:10.1.1.418.6870",
• rights = "Metadata may be used without restrictions as long as the oai identifier remains attached to it.",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.418.6870",
• URL = "http://www.ijera.com/papers/Vol3_issue3/GC3310851089.pdf",

}

Genetic Programming entries for Silvia Trif Adrian Visoiu

Citations