Figure 3 shows the mean classification accuracy of single trial power (blue diamonds) and rank spectra (green squares) in a single dataset as a function of the number of frequency bins (features) used.
Table 2 Mean classification accuracy at θ=0°.
MDR has been designed for binary classification ranking.
MDR performs very well on the classification ranking of ALL.
Hewett et al. [ 23] investigated the possibility of making tumor classification more informative by using a method for classification ranking.
Mean rank scores were calculated, test ranks compared within groups and significance determined by the Wilcoxon rank test.
For example, if the classification were operating at chance level, one would expect a mean normalized rank accuracy of 0.50, indicating that the correct word appeared on average between the 30th and 31st position in the classifier's output of a ranked list of 60 items.
The mean squared rank was chosen based on a survey of statistical approaches for gene set analysis [ 34], and out-performed other summary statistics in a series of classification benchmarks based on tissue-specific pathway expression (see benchmarking section below).
Respondents assigned a lower priority ranking to studies of overlap syndrome (mean rank 4.4), ocular chronic GVHD (mean rank 5.3), oral chronic GVHD (mean rank 5.4), or vaginal GVHD (mean rank 6.7).
