Comparative studies indicate that in terms of AUC and minority class accuracy, the weighted rough set based method is better than the re-sampling and filtering based methods, and is comparable to the decision tree and SVM based methods.
The results shown in Figure 7a reveals that our leaf orientation based method is better (with shorter distance to actual plant center) than the center of mass approach (p-value 0.18 with a two-tailed t-test with unequal variance [ 34]).
In particular, the artificial intelligence-based methods are better suited to discover strong nonlinearities between the set of descriptors and a given biological activity (or property) and can overcome some limitations of classic descriptor selection methods [8].
Both the local and the global structure-based methods are better at predicting molecular function than at predicting biological process and cellular component (Figure 2).
Thus, contrary to previous reports, our results indicate that conservation-based methods are better at predicting strong than weak binding sites.
Although the predictions generated by the structure-based methods are better than those of the sequence-based methods, the latter methods can be applied to a much wider range of problems where the structural information is unavailable.
While laboratory tests can be, and are, used to evaluate basic performance characteristics of athletes in most individual sports, in a more specific approach, field-based methods are better suited to the demands of complex intermittent sports like tennis.
3 5 6 While laboratory tests are used to evaluate basic performance characteristics in most individual sports, field-based methods are better suited to the demands of complex intermittent sports like tennis, since the variability in energy system, muscle group and skill incorporated in their performance is difficult to replicate in the laboratory.
Together with Table 2, the results suggest that different sampling schemes are more distinguishable when the users' collective attention exhibit sharp contrast before and after the event: for the Paris attacks and the Brussels shooting, CoPerplexity_RW based method is 3-6 times better than CommonTag_RW when considering the fraction of the 1st rank.
When we compare the results for different datasets (for example Figure 3, and Figure 8, Figure 9 from Appendix), we can see, that in all cases the two class decomposition based gene selection methods are better for different number of selected genes, when we consider the accuracy rate into account.
