Maximum entropy as a special case of the minimum description length criterion
IEEE Transactions on Information Theory
A maximum decisional efficiency estimation principle
Management Science
The potential use of DEA for credit applicant acceptance systems
Computers and Operations Research - Special issue on data envelopment analysis
Aggregating multiple expert data for linear case valuation models using the MDE principle
Decision Support Systems - Special issue: expertise and modeling expert decision making
An acceptance system decision rule with data envelopment analysis
Computers and Operations Research
Variance and Bias for General Loss Functions
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A Data Envelopment Analysis-Based Approach for Data Preprocessing
IEEE Transactions on Knowledge and Data Engineering
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
A hybrid radial basis function and data envelopment analysis neural network for classification
Computers and Operations Research
Computers and Industrial Engineering
Hi-index | 12.05 |
In this paper, we use data envelopment analysis (DEA) to preprocess training data cases before the maximum decisional efficiency (MDE) principle is used to estimate discriminant function parameters. Using an example from the literature and simulated datasets, we compare the performance of DEA-MDE procedure for parameter estimation with traditional MDE procedure without data preprocessing. The results of our experiments indicate that the DEA-MDE procedure eliminates some inconsistencies caused by MDE principle, provides results that are consistent with an ensemble of expert decisions, reduces dimensionality of examples used in training datasets, and performs equal to or better than the MDE procedure for holdout sample tests. The DEA-MDE procedure appears to be sensitive to class data distribution and best results are obtained when a class data distribution is exponential.