Sentence examples for the class separation from inspiring English sources

The singular values (Fig. 2d) representing the performance of the class separation showed the two major components LD1 (first component of LDA; corresponding to A and B) and LD2 (U, D) in contrast to the other minor ones (LD3, 4, and 5).

In this context, the current paper puts forward a two-step hybridized methodology, where learning is accurately performed by the SVMs and a comprehensible emulation of the resulting decision model is generated by EAs in the form of propositional rules, while referring only those indicators that highly influence the class separation.

The class separation for the gender estimation task was straightforward.

This increases the class separation and is illustrated by way of an example, two opcodes pop and ret are used as they demonstrate the characteristics of kernel mapping.

The study of the variables responsible for the class separation observed in Fig. 3a could be determined by the analysis of the p(corr) in the S-line plot (Fig. 3b).

For example, from Fig. 4, it is seen that channels such as 4, 12, 15, 19, and 30, possess t statistic values that maximize the class separation than other channels with lower statistic.

We further aimed to identify the metabolites that significantly contributed to the class separations in the LDA plot between the paraquat treated cells vs the control and vs other treatment groups.

The discriminant functions yielding the best class separation were searched using the following statistical and neural-network-based classifiers: Gaussian (quadratic discrimination rule), linear (normal based), nearest mean class, nearest neighbor, k-nearest neighbor, binary decision tree, radial basis functions and multilayer perceptron neural networks.

After the classification process the minimun subset of genes that allow the best class separation were selected: 9 genes for ALL, 5 for AML, 1 for CLL, and 5 for CML (blue-shaded boxes in Figure 1C; detailed information about these genes is included in Additional File 1).

The lipid class separation achieved with HILIC in combination with the sub-5-ppm mass accuracy attained with internal calibration was able to accurately identify lipid metabolites without running copious authentic standards.

As we can see from this results, we achieved the best class separation of in-domain and out-of-domain data in combination of Okapi BM25 weighting with similarity based on computing Jaccard correlation index.