A data envelopment analysis-based approach for data preprocessing

Parag C. Pendharkar

doi:10.1109/TKDE.2005.155

A data envelopment analysis-based approach for data preprocessing

Parag C. Pendharkar

School of Business Administration (Harrisburg)

Research output: Contribution to journal › Article › peer-review

37 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1379-1388
Number of pages	10
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	17
Issue number	10
DOIs	https://doi.org/10.1109/TKDE.2005.155
State	Published - Oct 2005

All Science Journal Classification (ASJC) codes

Information Systems
Computer Science Applications
Computational Theory and Mathematics

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10.1109/TKDE.2005.155

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A data envelopment analysis-based approach for data preprocessing

Abstract

All Science Journal Classification (ASJC) codes

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