Cost-Sensitive Learning and the Class Imbalance Problem

TLDR

Cost‑sensitive learning in data mining incorporates misclassification costs, unlike cost‑insensitive learning which ignores them. The aim of cost‑sensitive learning is to minimize total misclassification cost while achieving high classification accuracy.

Abstract

Cost-Sensitive Learning is a type of learning in data mining that takes the misclassification costs (and possibly other types of cost) into consideration. The goal of this type of learning is to minimize the total cost. The key difference between cost-sensitive learning and cost-insensitive learning is that cost-sensitive learning treats the different misclassifications differently. Costinsensitive learning does not take the misclassification costs into consideration. The goal of this type of learning is to pursue a high accuracy of classifying examples into a set of known classes.