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.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence