An Investigation of the Effects of Correlation, Autocorrelation, and Sample Size in Classifier Fusion

Nathan J. Leap

ISBN 10: 1288326947 / ISBN 13: 9781288326945

Published by BIBLIOSCHOLAR, 2012

About this Item

Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | 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.

Seller Inventory # 23538830/2

Bibliographic Details

• Title: An Investigation of the Effects of Correlation, Autocorrelation, and Sample Size in Classifier Fusion
• Publisher: BIBLIOSCHOLAR
• Publication Date: 2012
• Language: English
• ISBN 10: 1288326947
• ISBN 13: 9781288326945
• Binding: Soft cover
• Condition: Sehr gut
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.