article · IEEE Transactions on Knowledge and Data Engineering · 2005

A data envelopment analysis-based approach for data preprocessing

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Abstract

In this paper, we show how the data envelopment analysis (DEA) model might be useful to screen training data so a subset of examples that satisfy monotonicity property can be identified. Using real-world health care and software engineering data, managerial monotonicity assumption, and artificial neural network (ANN) as a forecasting model, we illustrate that DEA-based data screening of training data improves forecasting accuracy of an ANN.

Cite this paper

APA 7

Pendharkar, P. C. (2005). A data envelopment analysis-based approach for data preprocessing. IEEE Transactions on Knowledge and Data Engineering, 17(10), 1379–1388. https://doi.org/10.1109/tkde.2005.155

MLA 9

Pendharkar, Parag C. “A data envelopment analysis-based approach for data preprocessing.” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 10, 2005, pp. 1379–88, https://doi.org/10.1109/tkde.2005.155.

Chicago

Pendharkar, Parag C. 2005. “A data envelopment analysis-based approach for data preprocessing.” IEEE Transactions on Knowledge and Data Engineering 17 (10): 1379–88. https://doi.org/10.1109/tkde.2005.155.

Harvard

Pendharkar, P.C. (2005) ‘A data envelopment analysis-based approach for data preprocessing’, IEEE Transactions on Knowledge and Data Engineering, 17(10), pp. 1379–1388. Available at: https://doi.org/10.1109/tkde.2005.155.

BibTeX

@article{pendharkar2005data,
  title = {{A data envelopment analysis-based approach for data preprocessing}},
  author = {Pendharkar, Parag C.},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  volume = {17},
  number = {10},
  pages = {1379--1388},
  year = {2005},
  doi = {10.1109/tkde.2005.155}
}

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