Resampling Methods for Error Rate Estimation in Discriminant Analysis
Part of the
Studies in Classification, Data Analysis, and Knowledge Organization
book series (STUDIES CLASS)
The performance of resampling methods, like the bootstrap or cross-validation methods, was investigated for estimating the error rates in linear and quadratic discriminant analyses. A Monte Carlo experiment was carried out under the assumption that two population distributions were characterized by a mixture of two multivariate normal distributions. Simulation results indicated that the bootstrap method gave good performance in the case of the linear discriminant function, but it was a little biased when the quadratic discriminant function was used. Cross-validation method was superior in regard to the unbiasedness, and the 0.632 bootstrap estimator outperformed in regard to the mean square error. The methods for error rate estimation were also examined through the analysis of real data in medical diagnosis.
KeywordsMean Square Error Linear Discriminant Analysis Discriminant Function Multivariate Normal Distribution Quadratic Discriminant Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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