Types of Cost in Inductive Concept Learning

TL;DR

This paper proposes a taxonomy of various cost types involved in inductive concept learning, aiming to organize existing research and encourage comprehensive exploration of cost-sensitive learning beyond misclassification errors.

Contribution

It introduces a detailed taxonomy of cost types in inductive concept learning, addressing a gap in the literature and guiding future research.

Findings

Developed a taxonomy of cost types in concept learning

Highlighted the neglect of various cost types in existing research

Encouraged broader investigation of cost-sensitive learning

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.