Optimization driven data mining and credit scoring

Robert L. Grossman, H. Vincent Poor

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

An optimization tree approach to the mining of very extensive and complex databases for performance-optimizing opportunities is described. This methodology is based on a combination of three innovations: a data management system designed explicitly for data-intensive computing; a distributed algorithm for growing classification and regression trees (CART); and a tree-based stochastic programming paradigm for the selection of control attributes to optimize a specified objective function. This methodology provides a general technique for optimization in financial applications that is scalable as the number of objects in the database and as the number of attributes per object grow. This scalability allows for a complete data-driven analysis of large-scale data sets, without the need to restrict attention to sparsely sampled data sets that limits previous methods.

Original languageEnglish (US)
Pages104-110
Number of pages7
StatePublished - Dec 1 1996
Externally publishedYes
EventProceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr - New York, NY, USA
Duration: Mar 24 1996Mar 26 1996

Other

OtherProceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr
CityNew York, NY, USA
Period3/24/963/26/96

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Economics, Econometrics and Finance(all)
  • Engineering(all)

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    Grossman, R. L., & Poor, H. V. (1996). Optimization driven data mining and credit scoring. 104-110. Paper presented at Proceedings of the IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering, CIFEr, New York, NY, USA, .