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Comparing with the state-of-the-art methods, the classification accuracies demonstrate the effectiveness of the proposed TDCAF.
In the process of classification by decision tree methods, the classification rules are easily obtained, moreover these rules are clear and easy to understand for astronomers.
For the NNSG and GFHF methods, the classification method is as indicated in the corresponding research paper in (Zhou et al. 2004).
As shown in Table 5, with the informative genes selected by the five feature-selection methods, the classification performances of NB and KNN were significantly improved.
We also show that if we remove the influence of force changes on the contralateral hand (see Materials and Methods), the classification accuracy for the ipsilateral fingers across all functional finger ROIs was reduced by <1%.
For all four ranking methods, the classification performance shows the general trend of higher AUCs with a moderate number of SNPs used in the classifier, and lower AUCs at very small and very large numbers of SNPs.
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We then separately attacked each image by means of four methods: the classification-based attack of section 4.3, the iteration of RMD (δ = 2), the iteration of smoothing (σ = 0.7, h = 3), and the iteration of collage.
In the IPF method, the classification algorithm using the observed velocity amplitude improved its ability to discriminate between earthquake occurrences.
Also, in the proposed method, the classification of human is done with other objects such as vehicles.
They show that feature selection methods improve the classification accuracy.
Algorithm requires multiple weighted methods (until the classification ability down). .
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