Title :
Optimization driven data mining and credit scoring
Author :
Grossman, Robert L. ; Poor, H. Vincent
Author_Institution :
Magnify Inc., Oak Park, IL, USA
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
Keywords :
distributed algorithms; financial data processing; knowledge acquisition; mathematical programming; stochastic programming; tree data structures; trees (mathematics); classification and regression trees; control attributes; credit scoring; data driven analysis; data intensive computing; data management system; distributed algorithm; financial applications; objective function; optimization driven data mining; optimization tree approach; performance optimizing opportunities; tree based stochastic programming paradigm; Algorithm design and analysis; Classification tree analysis; Data mining; Databases; Distributed algorithms; Distributed computing; Innovation management; Regression tree analysis; Stochastic systems; Technological innovation;
Conference_Titel :
Computational Intelligence for Financial Engineering, 1996., Proceedings of the IEEE/IAFE 1996 Conference on
Conference_Location :
New York City, NY
Print_ISBN :
0-7803-3236-9
DOI :
10.1109/CIFER.1996.501831
