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Details for article 2 of 7 found articles
| Title: |
An investigation of the effects of correlation and autocorrelation on classifier fusion and optimal classifier ensembles |
| Author: |
Leap, Nathan J. Clemans, Paul P. Bauer, Kenneth W. Oxley, Mark E. |
| Appeared in: |
International journal of general systems |
| Paging: | Volume 37 (2008) nr. 4 pages 475-498 |
| Year: |
2008-08 |
| Contents: |
The purpose of this research was to study various fusion strategies where the levels of correlation between features and auto-correlation within features could be controlled. The fusion strategies were chosen to reflect decision-level fusion (ISOC and ROC), feature level fusion, via a single Generalized Regression Neural Network (GRNN) employing all available features, and an intermediate level of fusion that employed the outputs of individual classifiers, in this case posterior probability estimates, before they are subjected to thresholds and mapped into decisions. This latter scheme involved fusing the posterior probability estimates by employing them as features in a probabilistic neural network. Correlation was injected into the data set both within a feature set (auto-correlation) and across feature sets, and sample size was varied for a two class problem. The fusion methods were then extended to three classifiers, and a method is demonstrated that selects the optimal classifier ensemble. |
| Publisher: |
Taylor & Francis |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 2 of 7 found articles
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