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Although the conventional evaluation criterion is the classification accuracy, selecting a stable feature set that is not sensitive to the variance in dataset may provide more scientific insights.
That means the first two factors have more importance in explaining the variance in dataset than the other three factors combined.
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We hope that the proposed method provides additional insights into the pathogenesis of other diseases using hundreds of genetic variance in datasets.
For this to be a standalone test, we then select the datasets based on the variance in the dataset and only use datasets with high variations.
The second treatment of dataset consisted in a normalization based on the variance in the dataset as described previously [ 9].
Samples were obtained from different human subjects, thus the biological variance in this dataset is expected to be large (no biological variation is present in the cell line dataset).
The four factors explain 92.05 % of the total variance in the dataset.
The main reason for these results is due the large intraclass variance in this dataset.
Factor analysis identified five factors accounting for 79.65 % of the total variance in the dataset.
The first component depicted between 53% and 70% of the total variance in the dataset for the different years.
LDA [30] maximizes the ratio of between-class variance to the within-class variance in any dataset.
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