Abstract

For a given regression model, each individual prediction may be more or less accurate. The average accuracy of the system cannot provide the error estimate for a single particular prediction, which could be used to correct the prediction to a more accurate value. We propose a method for correction of the regression predictions that is based on the sensitivity analysis approach. Using predictions, gained in sensitivity analysis procedure, we build a secondary regression predictor whose task is to predict the signed error of the prediction which was made using the original regression model. We test the proposed methodology using four regression models: locally weighted regression, linear regression, regression trees and neural networks. The results of our experiments indicate significant increase of prediction accuracy in more than 20

Keywords

MSC

68T05, 68T37, 93D25

Cite this article

@article{cai-1813,
  author  = {Zoran Bosnić and Igor Kononenko},
  title   = {Correction of Regression Predictions Using the Secondary Learner on the Sensitivity Analysis Outputs},
  journal = {Computing and Informatics},
  volume  = {29},
  number  = {6},
  pages   = {929--946},
  year    = {2010},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2010/6/correction-of-regression-predictions-using-the-secondary-learner-on-the-sensitivity-analysis-outputs/}
}
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