Abstract
Support Vector Machines (SVM) are among the
best-known machine learning methods, with broad
use in different scientific areas. However, one necessary pre-processing phase for SVM is normalization (scaling) of features, since SVM is not invariant to the scales of the features’ spaces, i.e.,
different ways of scaling may lead to different results. We define a more robust decision-making approach for binary classification, in which one sample strongly belongs to a class if it belongs to that
class for all possible rescalings of features. We derive a way of characterising the approach for binary
SVM that allows determining when an instance
strongly belongs to a class and when the classifi-
cation is invariant to rescaling. The characterisation leads to a computational method to determine
whether one sample is strongly positive, strongly
negative or neither. Our experimental results back
up the intuition that being strongly positive suggests stronger confidence that an instance really is
positive