Table 1.
Full name and abbreviation for some used terms.
Table 2.
Algorithm: INysCK.
Table 3.
Algorithm: MINysCK.
Table 4.
Description of the used UCI data sets.
Table 5.
Used classifiers.
Fig 1.
Accuracy with related classifiers and CK-based methods on used single-view data sets.
CK-related method in italic represents baseline one and one in bold denotes the proposed one. For classifiers, SVM is used as the baseline one and we just clarify this point in words rather than in font. In other figures and tables, we have similar representations.
Fig 2.
Accuracy with related classifiers and CK-based methods on used multi-view data sets.
CK-related method in italic represents baseline one and ones in bold denote the proposed ones. For classifiers, MSVM is used as the baseline one and we just clarify this point in words rather than in font. In other figures and tables, we have similar representations.
Table 6.
Comparison about time (in seconds) cost for the three NysCK-related methods.
Fig 3.
Distributions of samples with different CK-related methods on a binary-class data set.
Table 7.
The numbers of iterations comparisons.
Fig 4.
The average Rademacher complexity comparison.
Table 8.
Average rank comparisons for different CK-related methods and classifiers.
Table 9.
Critical values for the two-tailed Nemenyi test.
Fig 5.
Average influence of ratio of training samples on accuracy with INysCK and MINysCK and corresponding classifiers used.