{"type":"ScholarlyArticle","title":"An Empirical Study of the Noise Impact on Cost-Sensitive Learning","authors":[{"name":"Xingquan Zhu","slug":"xingquan-zhu","given":"Xingquan","family":"Zhu","affiliation":""},{"name":"Xindong Wu","slug":"xindong-wu","given":"Xindong","family":"Wu","affiliation":""},{"name":"Taghi M. Khoshgoftaar","slug":"taghi-m-khoshgoftaar","given":"Taghi M.","family":"Khoshgoftaar","affiliation":""},{"name":"Yong Shi","slug":"yong-shi","given":"Yong","family":"Shi","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2007/189","pdf":"https://ijcai.org/proceedings/2007/0189.pdf","markdown":"https://ijcai.org/proceedings/2007/189.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/189","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":189,"pages":{"first":1168,"last":1173},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}