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First, we classified samples according to sampling year and viral subtype, and then performed a Chi-squared analysis for trends.
We identified a panel of 11 proteins and combined them into a classifier that best classified samples into the two disease groups.
With the replaced labels and the originally used features the knn classifier reports the fraction of correctly classified samples in the neighborhood of the query instance.
This was repeated until all pairs had been left out once and the accuracy of the classifier was determined by the correctly classified samples.
The visual features of the object classifier are evolved to optimise the proportion of correctly classified samples.
The two-stage classifier with 16 Gaussian mixtures and the mean of the normalized kurtosis classified samples with a 96.96% accuracy were obtained.
Therefore, extra attention is given to the wrongly classified samples.
The error rate is calculated based on the classified samples excluding any outliers.
In order to achieve it, J48 requires a set of training data of already classified samples.
A classification accuracy Acc gives the percentage of correctly classified samples regardless their class.
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In order to achieve balanced study groups, we consecutively selected pre-classified samples for each stage (Table 1) from the tissue bank.
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