On Classification with Pairwise Comparisons, Support Vector Machines and Feature Domain Overlapping

Most existing classification algorithms either consider all features as equally important (equal weights), or do not analyze the consistency of weights assigned to features. When features are not equally important, assigning consistent weights is not an obvious task. In general, we have two cases. The first case assumes that a given sample of data does not contain any clues about the importance of features, so the weights are provided by a pool …