{"type":"ScholarlyArticle","title":"Distribution-Aware Online Classifiers","authors":[{"name":"Tam T. Nguyen","slug":"tam-t-nguyen","given":"Tam T.","family":"Nguyen","affiliation":""},{"name":"Kuiyu Chang","slug":"kuiyu-chang","given":"Kuiyu","family":"Chang","affiliation":""},{"name":"Siu Cheung Hui","slug":"siu-cheung-hui","given":"Siu Cheung","family":"Hui","affiliation":""}],"abstract":"We propose a family of Passive-Aggressive Mahalanobis (PAM) algorithms, which are incremental (online) binary classifiers that consider the distribution of data. PAM is in fact a generalization of the Passive-Aggressive (PA) algorithms to handle data distributions that can be represented by a covariance matrix. The update equations for PAM are derived and theoretical error loss bounds computed. We benchmarked PAM against the original PA-I, PA-II, and Confidence Weighted (CW) learning. Although PAM somewhat resembles CW in its update equations, PA minimizes differences in the weights while CW minimizes differences in weight distributions. Results on 8 classification datasets, which include a real-life micro-blog sentiment classification task, show that PAM consistently outperformed its competitors, most notably CW. This shows that a simple approach like PAM is more practical in real-life classification tasks, compared to more elegant and sophisticated approaches like CW.","doi":"10.5591/978-1-57735-516-8/IJCAI11-241","url":"https://ijcai.org/proceedings/2011/241","pdf":"https://ijcai.org/proceedings/2011/0241.pdf","markdown":"https://ijcai.org/proceedings/2011/241.md","bibtex":"https://ijcai.org/proceedings/2011/bibtex/241","volume":{"slug":"2011","title":"Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2011","url":"https://ijcai.org/proceedings/2011/","year":2011,"location":"Barcelona, Catalonia, Spain","dates":"16–22 July 2011","editors":"Toby Walsh","isbn":"978-1-57735-512-0; 978-1-57735-513-7; 978-1-57735-514-4; 978-1-57735-515-1; 978-1-57735-516-8","publisher":"AAAI Press/International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":241,"pages":{"first":1427,"last":1431},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}