Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InCollection{masand:1996:aigp2,

• author = "Brij Masand and Gregory Piatesky-Shapiro",
• title = "Discovering Time Oriented Abstractions in Historical Data to Optimize Decision Tree Classification",
• booktitle = "Advances in Genetic Programming 2",
• publisher = "MIT Press",
• year = "1996",
• editor = "Peter J. Angeline and K. E. {Kinnear, Jr.}",
• pages = "489--498",
• chapter = "24",
• address = "Cambridge, MA, USA",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-262-01158-1",
• URL = "http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6277523",
• DOI = "doi:10.7551/mitpress/1109.003.0031",
• size = "10 pages",
• abstract = "This paper explores the synergy between OP and decision tree-based classification. We are addressing the problem of identifying 'good' customers (e.g those who respond to special offers) by analysing historical customer billing data, using decision tree classifiers such as C4.5 [Quinlan 1993) and optimising that performance using OP [Koza 1992]. One difficult issue is how to transform and abstract raw historical data from several months for the purpose of analysis. We ad dress this by using OP to discover time oriented data abstractions of data. that enable improved prediction performance. than possible with the raw data alone. We also contrast the performance improvement obtained by generating random populations with comparable computational effort vs. OP evolution on smaller populations. Using C4.5 alone we are able to get a prediction error of about 38percent (on a 50-50percent test set of non-responders/responders) Using the additional derived fields from raw billing data, we are able to reduce the error to 35.9percent, a significant reduction for this domain. Each 1percent of improved performance (on real data) is worth about $1 million in potential increased revenues.",

}

Genetic Programming entries for Brij Masand Gregory Piatesky-Shapiro

Citations