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Compare this with the optimum for the unreduced approach, which gave an accuracy of 78.5% for the training data and 83.9% for the test data. The 3-class dataset is a more challenging problem, as is re ected in the overall lower classi cation accuracies produced. Both fuzzy-rough methods, FQR and FRFS, choose 11 out of the original 38 features (but not the same features). The results of these approaches can be seen in Figure 12.13. Again, the results show that both FQR and FRFS outperform the unreduced approach on the whole. The best classi cation accuracy obtained for FQR was 72.1% using the training data,. Using Barcode printer for ASP.NET Control to generate, create .Related: Generate Codabar ASP.NET , UPC-E Printing Java , Code 39 Generator VB.NET However, several other companies who make browsers are also involved in the WebM project, and so in he. 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