An Investigation of the Effects of Correlation, Autocorrelation, and Sample Size in Classifier Fusion
Master's thesis Jun 2003-Mar 2004
AIR FORCE INST OF TECH WRIGHT-PATTERSON AFB OH SCHOOL OF ENGINEERING AND MANAGEMENT
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This thesis extends the research found in Storm, Bauer, and Oxley, 2003. Data correlation effects and sample size effects on three classifier fusion techniques and one data fusion technique were investigated. Identification System Operating Characteristic Fusion Haspert, 2000, the Receiver Operating Characteristic Within Fusion method Oxley and Bauer, 2002, and a Probabilistic Neural Network were the three classifier fusion techniques a Generalized Regression Neural Network was the data fusion technique. Correlation was injected into the data set both within a feature set autocorrelation and across feature sets for a variety of classification problems, and sample size was varied throughout. Total Probability of Misclassification TPM was calculated for some problems to show the effect of correlation on TPM. Feature selection was performed in some experiments to show the effects of selecting only certain features. Finally, experiments were designed and analyzed using analysis of variance to identify what factors had the most significant impact on fusion algorithm performance.
- Statistics and Probability
- Computer Programming and Software