Adaptive Learning for the Resource-Constrained Classification Problem

Classification applications are typically associated with misclassification costs and benefits as a result of incorrect and correct classification, respectively. Many studies have focused on cost-sensitive classification approaches [7, 8, 9, 10, 11, 12] in an effort to reduce the costs of misclassification. We illustrate the concept of imbalanced misclassification costs using the current and real-world example of classifying COVID-19 patients. Incorrectly classifying an ill patient as healthy may put this patient's life at risk as well as others by allowing the ill person to circulate among healthy persons and infect them (an intangible cost, usually determined by the judicial system). Classifying a healthy individual as a COVID-19 patient, on the other hand, may lead to unnecessary treatment, misuse of medical resources and cause unnecessary financial hardship to the individual and the general economy. Many studies have applied cost-sensitive approaches to handling imbalanced classification problems [13, 14] where the decision maker is interested in detecting the positive cases. There are four main approaches for making a classifier cost-sensitive: (i) changing the distribution of classes using over-and under-sampling within the training data set (i.e., preprocessing of the training data) to reduce misclassification costs [7, 8], denoted hereafter approach A1; (ii) changing the data set according to the misclassified samples of the cost-insensitive classifiers and their error costs (post-processing the training data) using a boosting approach in ensemble learning methods [12, 15], denoted hereafter approach A2; (iii) incorporating meta-learning methods on outputs of cost-insensitive learners using threshold driven techniques in favor of utilizing the probability estimations for the classes [7, 8, 16, 17], hereafter denoted A3; (iv) directly incorporating cost-sensitive capabilities into a learning algorithm, i.e., an algorithm-level solution that adapts existing learning methods so they are biased towards classes with high misclassification costs, usually presented by minority classes [8, 18].