A data envelopment analysis-based approach for data preprocessing

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.

[1]  LiMin Fu,et al.  Rule Generation from Neural Networks , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[2]  Siddhartha Bhattacharyya,et al.  Inductive, Evolutionary, and Neural Computing Techniques for Discrimination: A Comparative Study* , 1998 .

[3]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[4]  Michael Lee Steib,et al.  Expert systems for guiding backpropagation training of layered perceptrons , 1991 .

[5]  Yakar Kannai.  A characterization of monotone individual demand functions , 1989 .

[6]  Selwyn Piramuthu,et al.  Using Feature Construction to Improve the Performance of Neural Networks , 1998 .

[7]  Marvin D. Troutt,et al.  The potential use of DEA for credit applicant acceptance systems , 1996, Comput. Oper. Res..

[8]  Larry R. Medsker,et al.  Hybrid Intelligent Systems , 1995, Springer US.

[9]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[10]  T. E. Marshall,et al.  Relating information technology investment to organizational performance: a causal model analysis , 1997 .

[11]  M. Caudill.  Using neural nets: representing knowledge, part 1 , 1989 .

[12]  Li-Min Fu,et al.  Knowledge-based connectionism for revising domain theories , 1993, IEEE Trans. Syst. Man Cybern..

[13]  James Bielak.  Improving Size Estimates Using Historical Data , 2000, IEEE Softw..

[14]  Maureen Caudill,et al.  Neural network training tips and techniques , 1991 .

[15]  B. Curry,et al.  Neural networks: a need for caution , 1997 .

[16]  W. A. Wright,et al.  Bayesian approach to neural-network modeling with input uncertainty , 1999, IEEE Trans. Neural Networks.

[17]  Parag C. Pendharkar,et al.  An empirical study of design and testing of hybrid evolutionary–neural approach for classification , 2001 .

[18]  A. Roli.  Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[19]  Michael Y. Hu,et al.  Two-Group Classification Using Neural Networks* , 1993 .

[20]  Daniel Santín,et al.  The measurement of technical efficiency: a neural network approach , 2004 .

[21]  Robert J. Kauffman,et al.  Comparing the Modeling Performance of Regression and Neural Networks as Data Quality Varies: A Business Value Approach , 1993, J. Manag. Inf. Syst..

[22]  Yann LeCun,et al.  Improving the convergence of back-propagation learning with second-order methods , 1989 .

[23]  Efraim Turban,et al.  Integrating Expert Systems and Neural Computing for Decision Support , 1994, HICSS.

[24]  Antreas D. Athanassopoulos,et al.  A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units , 1996 .

[25]  G. David Garson,et al.  Interpreting neural-network connection weights , 1991 .

[26]  Marvin D. Troutt,et al.  A maximum decisional efficiency estimation principle , 1995 .

[27]  Shouhong Wang.  The unpredictability of standard back propagation neural networks in classification applications , 1995 .

[28]  Pascal Neveu,et al.  Bayesian nonlinear model selection and neural networks: a conjugate prior approach , 2000, IEEE Trans. Neural Networks Learn. Syst..

[29]  Christian Kanzow,et al.  A continuation method for (strongly) monotone variational inequalities , 1998, Math. Program..

[30]  Bernard W. Silverman,et al.  Monotone discriminant functions and their applications in rheumatology , 1997 .

[31]  A. U.S.,et al.  Measuring the efficiency of decision making units , 2003 .

[32]  J. Quah.  The Monotonicity of Individual and Market Demand , 2000 .

[33]  Larry R. Medsker,et al.  Microcomputer applications of hybrid intelligent systems , 1996 .

[34]  Shouhong Wang,et al.  Adaptive non-parametric efficiency frontier analysis: a neural-network-based model , 2003, Comput. Oper. Res..