---
title: "An Empirical Study of the Noise Impact on Cost-Sensitive Learning"
authors:
  - "Xingquan Zhu"
  - "Xindong Wu"
  - "Taghi M. Khoshgoftaar"
  - "Yong Shi"
doi: ""
url: "https://ijcai.org/proceedings/2007/189"
pdf: "https://ijcai.org/proceedings/2007/0189.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "1168-1173"
track: ""
keywords:
---

# An Empirical Study of the Noise Impact on Cost-Sensitive Learning

Xingquan Zhu, Xindong Wu, Taghi M. Khoshgoftaar, Yong Shi

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 1168-1173.

## Abstract

In this paper, we perform an empirical study of the impact of noise on cost-sensitive (CS) learning, through observations on how a CS learner reacts to the mislabeled training examples in terms of mis-classification cost and classification accuracy. Our empirical results and theoretical analysis indicate that mislabeled training examples can raise serious concerns for cost-sensitive classification, especially when misclassifying some classes becomes ex-tremely expensive. Compared to general inductive learning, the problem of noise handling and data cleansing is more crucial, and should be carefully investigated to ensure the success of CS learning.