A data envelopment analysis-based approach for data preprocessing

Parag C. Pendharkar

50 citations0/0 files storedIEEE Transactions on Knowledge and Data Engineering

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Efficiency Analysis Using DEA Data Mining and Machine Learning Applications Machine Learning and Data Classification

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.

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1978Measuring the efficiency of decision making units 1984Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis 1996A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units 1993Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach 2004The measurement of technical efficiency: a neural network approach 1997Relating information technology investment to organizational performance: a causal model analysis 2002Adaptive non-parametric efficiency frontier analysis: a neural-network-based model 1996The potential use of DEA for credit applicant acceptance systems

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