Types of Cost in Inductive Concept Learning

TLDR

Inductive concept learning assigns cases to discrete classes, yet most literature ignores the many types of cost beyond misclassification errors, despite their importance in real‑world applications. The authors aim to create a taxonomy of the various cost types in inductive concept learning to organize the literature and encourage deeper investigation. They develop the taxonomy by systematically categorizing cost types, building on prior work that focused mainly on misclassification costs.

Abstract

Inductive concept learning is the task of learning to assign cases to a discrete set of classes. In real-world applications of concept learning, there are many different types of cost involved. The majority of the machine learning literature ignores all types of cost (unless accuracy is interpreted as a type of cost measure). A few papers have investigated the cost of misclassification errors. Very few papers have examined the many other types of cost. In this paper, we attempt to create a taxonomy of the different types of cost that are involved in inductive concept learning. This taxonomy may help to organize the literature on cost-sensitive learning. We hope that it will inspire researchers to investigate all types of cost in inductive concept learning in more depth.