SVM, especially when the feature set is optimized by dimensionality reduction, is a sensitive classification method that approaches the performance of the theoretical optimal classifier for classifying visual fields [ 16, 18, 24, 25].
Parameters optimal for classifying one type of gene family yielded poor performance when used for classifying the other (Figure 3).
Using MC-UPGMA with parameters optimal for classifying chemosensory genes in the C. elegans data set only, we observe a drop in performance when classifying the same genes in the context of the larger, five-species data set (Figure 4).
Clustering under the optimal condition classified 43 intramucosal gland-forming neoplasms of varying histological grades into 3 clusters: stable, unstable and intermediate.
The optimal method for classifying lymph node (LN) status in breast cancer patients is unknown.
The optimal method of classifying proteinuria status for study participants in whom it is measured multiple times is unknown, especially when the frequency of measurement varies between participants.
Again, the method does not make use of the structure of the network, nor does it provide a systemic account for the combined knowledge of pathways to reduce to an optimal set of classifying processes.
The CART modelling approach was used to rank important predictors of death and to identify complex interaction between predictors, and to identify optimal thresholds for classifying patients by mortality if a predictor was a continuous variable.
Then the well-trained classifier is used, with the optimal parameters, to classify the testing set.
Firstly, we construct an optimal system to classify the group-invariant solutions of (3) and the problem of finding an optimal subgroup is equivalent to that of finding an optimal system of sub-algebras.
