Interactive Martingale Boosting

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.

Authors

• Ashish Kulkarni
• Pushpak Burange
• Ganesh Ramakrishnan Indian Institute of Technology Bombay

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