Title :
A data envelopment analysis-based approach for data preprocessing
Author :
Pendharkar, Parag C.
Author_Institution :
Pennsylvania State Univ., Middletown, PA, USA
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.
Keywords :
data envelopment analysis; forecasting theory; health care; learning (artificial intelligence); neural nets; artificial neural network; data envelopment analysis; data preprocessing; forecasting model; health care data; managerial monotonicity assumption; software engineering data; Artificial neural networks; Data analysis; Data envelopment analysis; Data preprocessing; Engineering management; Management training; Medical services; Predictive models; Software engineering; Training data; Index Terms- Data envelopment analysis; artificial neural networks; data preprocessing.;
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
DOI :
10.1109/TKDE.2005.155
