An Empirical Study of the Noise Impact on Cost-Sensitive Learning
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.
@inproceedings{ijcai2007p189,
title = {An Empirical Study of the Noise Impact on Cost-Sensitive Learning},
author = {Zhu, Xingquan and Wu, Xindong and Khoshgoftaar, Taghi M. and Shi, Yong},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1168--1173},
year = {2007},
url = {https://ijcai.org/proceedings/2007/189},
}
TY - CPAPER TI - An Empirical Study of the Noise Impact on Cost-Sensitive Learning AU - Zhu, Xingquan AU - Wu, Xindong AU - Khoshgoftaar, Taghi M. AU - Shi, Yong T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1168 EP - 1173 UR - https://ijcai.org/proceedings/2007/189 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
