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As there is no closed form solution for setting the weights, the fit is done by numerical optimization with objective of maximizing classification accuracy.
It is essential to select a minimal number of relevant genes from microarray data while maximizing classification accuracy for the development of inexpensive diagnostic tests.
The NNTree is designed by splitting the non-terminal nodes of the tree by maximizing classification accuracy of the multilayer perceptron.
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The TLF ratio as a product of FLV and L/Smean ratios maximized classification accuracy at a threshold of 58%%.
HHPSO SVM feature selection algorithm (HHPSO SVM) aims to maximize classification accuracy (Max-Accuracy) and to minimize the size of selected features (Min-Size) simultaneously.
The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets.
The choice of the particular number of voxels (50) used as input was motivated by similar analyses in other datasets where 50 was the smallest number of voxels that maximized classification accuracy).
The panel sizes were chosen to maximize classification accuracy with an approximately minimal number of markers and are connected: the number of markers in P2 is equal to twice the number of markers in P1, and the number of markers in P3 is equal to three times the number of markers in P1.
However, the identification classifier is trained to maximize classification accuracy on identification.
This trend proved that EPSO is suitable for selecting a small number of genes from high-dimensional data (gene expression data) to maximize classification accuracy.
How would one obtain the optimal number that maximizes classification accuracy and minimize computational requirements?
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