We have extracted the optimal features identified in [13].
The neuro-tree classifier provides effective classification when optimal features are provided.
There have been other attempts to extract optimal features from signals.
Size, oligomer assembly and repetitiveness of epitopes are optimal features to induce strong immune responses.
Finally, SVM was applied for predicting protein subcellular locations based on these optimal features.
These optimal features are reported in dimensionless terms for this entire class of fin assemblies.
Genetic Algorithm (GA) selects the set of optimal features from this input set.
A training model was then built based on the Bayesian network to determine a subset of the optimal features.
Likely optimal features are selected from these fuzzified features using neighborhood rough sets, which utilize the neighborhood relative information.
Factor analysis was used to reduce the amount of redundant information in the highly dimensional features and extract the optimal features for the cluster analysis.
In each strategy, the extracted PolSAR features are combined and optimal features are selected by Genetic Algorithm (GA) and then a Support Vector Machine (SVM) classifier is applied.
