For the sake of clarification.
For the sake of improving binary classification, it is proposed to apply a bootstrapping procedure that involves learning on so-called "hard" examples [7, 19].
For the sake of completeness, the classifications are repeated using a linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k-nearest neighbor algorithm (k-NN), from which the results are compared with SVM.
For the sake of complete exploration, we produce classification results with the Weka (http://www.cs.waikato.ac.nz/ml/weka/) and scikit-learn (http://scikit-learn.org/stable/) implementation of LibSVM.
For the sake of simplicity, an extremely detailed sub-classification is avoided: anatomical variations of uterine, cervical and vaginal anomalies are grouped in sub-classes having as criterion the clinical significance of the abnormality.
For the sake of pragmatism, it has been chosen to organize such classification around a specific architecture for a Negotiation Support System which is being developed within a wider research project.
The classification problem was binarized for the sake of detecting features that have a subtle link with cognitive decline at different stages of the disease, also avoiding the need to further reduce the size of the dataset.
For the sake of simplicity, the scope of this article will only address binary classification.
To best manage these landscapes for the sake of both human and natural systems, SSF need to be considered a distinct land classification in their own right, not merely perceived as fuzzy boundaries around wild lands or urban areas.
For the sake of simplicity we present our results based on typical pro- and anti-inflammatory classifications of these inflammation biomarkers.
For the sake of simplicity, let us consider the results when the proposed features are applied to two-category classification problems.
