Title:
SYSTEM AND METHOD FOR OPTIMIZATION OF A DATABASE FOR THE TRAINING AND TESTING OF PREDICTION ALGORITHMS
Document Type and Number:
WIPO Patent Application WO2004063831
Kind Code:
A3
Abstract:
A system and method are provided for the training and testing of prediction algorithms. According to an exemplary embodiment of the invention the method generates optimum training, testing and/or validation data sets from a common general database by applying a genetic algorithm to populations of testing and training subsets used in connection with a given prediction algorithm. In exemplary embodiments the prediction algorithm operated upon is an artificial neural network. As well, in preferred exemplary embodiments, the most predictive independent variables of the records of the common database are automatically selected in a pre-processing phase. Such preprocessing phase applies a genetic algorithm to populations of prediction algorithms which vary as to number and content of input variables, where the prediction algorithms representing the selections of input variables which have the best testing performances and the minimum input variables are promoted for the processing of the new generations according to a defined selection algorithm.
Inventors:
BUSCEMA MASSIMO (IT)
Application Number:
PCT/EP2004/000157
Publication Date:
August 11, 2005
Filing Date:
January 13, 2004
Export Citation:
Assignee:
BRACCO IMAGING SPA (IT)
BUSCEMA MASSIMO (IT)
BUSCEMA MASSIMO (IT)
International Classes:
G06N3/08; (IPC1-7): G06N3/08; G06F19/00
Domestic Patent References:
| WO2001016881A2 | 2001-03-08 | |||
| WO2000007113A1 | 2000-02-10 |
Foreign References:
| US20020059154A1 | 2002-05-16 | |||
| US20020184569A1 | 2002-12-05 |
Other References:
PECK C C ET AL: "Genetic algorithm based input selection for a neural network function approximator with applications to SSME health monitoring", PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS (ICNN). SAN FRANCISCO, MAR. 28 - APR. 1, 1993, NEW YORK, IEEE, US, vol. VOL. 1, 28 March 1993 (1993-03-28), pages 1115 - 1122, XP010111746, ISBN: 0-7803-0999-5
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