{"type":"ScholarlyArticle","title":"Interactive Martingale Boosting","authors":[{"name":"Ashish Kulkarni","slug":"ashish-kulkarni","given":"Ashish","family":"Kulkarni","affiliation":""},{"name":"Pushpak Burange","slug":"pushpak-burange","given":"Pushpak","family":"Burange","affiliation":""},{"name":"Ganesh Ramakrishnan","slug":"ganesh-ramakrishnan","given":"Ganesh","family":"Ramakrishnan","affiliation":""}],"abstract":"We present an approach and a system that explores the application of interactive machine learning to a branching program-based boosting algorithm- Martingale Boosting. Typically, its performance is based on the ability of a learner to meet a fixed objective and does not account for preferences (e.g. low false positives) arising from an underlying classification problem. We use user preferences gathered on holdout data to guide the two-sided advantages of individual weak learners and tune them to meet these preferences. Extensive experiments show that while arbitrary preferences might be difficult to meet for a single classifier, a non-linear ensemble of classifiers as the one constructed by martingale boosting, performs better.","doi":null,"url":"https://ijcai.org/proceedings/2016/124","pdf":"https://ijcai.org/proceedings/2016/0124.pdf","markdown":"https://ijcai.org/proceedings/2016/124.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/124","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":124,"pages":{"first":841,"last":846},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}