Abstract
Classification has become a vital task in modern machine learning and
Artificial Intelligence applications, including smart sensing. Numerous
machine learning techniques are available to perform classification.
Similarly, numerous practices, such as feature selection (i.e.,
selection of a subset of descriptor variables that optimally describe
the output), are available to improve classifier performance. In this
paper, we consider the case of a given supervised learning classification
task that has to be performed making use of continuous-valued features.
It is assumed that an optimal subset of features has already been
selected. Therefore, no further feature reduction, or feature addition,
is to be carried out. Then, we attempt to improve the classification
performance by passing the given feature set through a transformation
that produces a new feature set which we have named the “Binary
Spectrum”. Via a case study example done on some Pulsed Eddy Current
sensor data captured from an infrastructure monitoring task, we
demonstrate how the classification accuracy of a Support Vector Machine
(SVM) classifier increases through the use of this Binary Spectrum
feature, indicating the feature transformation’s potential for broader
usage.