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This feature is attributed to the observed higher accuracy of boundary detection by the CRF model.
As evidenced by the test results, three aspects of advantages for the proposal in this work have been observed: higher accuracy, lower computational effort and superior numerical stability.
The observed higher accuracy for line B1 compared to B2 was therefore quite unexpected, also considering the observed similarities between these two lines (e.g. Figures 1 and 2) and their similar trait heritabilities.
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In this study, we have observed higher accuracies with QTL-linked markers compared to the random markers, especially in independent validation.
We observed higher accuracies with a few QTL-linked markers compared to several random markers for both TYM and SGN in this study (Table 1).
Similarly, in a recent study of wheat, Zhao et al. (2014) observed higher accuracies for heading time with marker-assisted selection using functional markers compared to genomic selection with random markers.
The observed high accuracy may have been due to the selection of items.
At short sampling times, as already observed, high accuracies can be achieved.
We also observed high accuracies among MLP classification and all feature selection methods with 94%, 92%, and 92% for SVM-RFE, CFS, and Chi Squared respectively.
We observe higher accuracies on simulated multifactorial than on knock-down data.
In lung cancer datasets we can observe high accuracy in Decision Tree classification methods (both J48 and ID3) with all feature selection methods.
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