Interactive Martingale Boosting
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. Pages 841-846.
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.
@inproceedings{ijcai2016p124,
title = {Interactive Martingale Boosting},
author = {Kulkarni, Ashish and Burange, Pushpak and Ramakrishnan, Ganesh},
booktitle = {Proceedings of the Twenty-Fifth International Joint Conference on
Artificial Intelligence, {IJCAI-16}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Subbarao Kambhampati},
pages = {841--846},
year = {2016},
month = {7},
url = {https://ijcai.org/proceedings/2016/124},
}
TY - CPAPER TI - Interactive Martingale Boosting AU - Kulkarni, Ashish AU - Burange, Pushpak AU - Ramakrishnan, Ganesh T2 - Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI-16 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2016 SP - 841 EP - 846 UR - https://ijcai.org/proceedings/2016/124 ER -Copied
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