---
title: "Interactive Martingale Boosting"
authors:
  - "Ashish Kulkarni"
  - "Pushpak Burange"
  - "Ganesh Ramakrishnan"
doi: ""
url: "https://ijcai.org/proceedings/2016/124"
pdf: "https://ijcai.org/proceedings/2016/0124.pdf"
venue: "Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence"
year: 2016
pages: "841-846"
track: ""
keywords:
---

# Interactive Martingale Boosting

Ashish Kulkarni, Pushpak Burange, Ganesh Ramakrishnan

*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.