Rudolph Triebel, Hugo Grimmett, and Ingmar Posner

2013 IEEE International Conference on Robotics and Automation (ICRA)

This paper concerns the recently introduced notion of introspective classification. We introduce a variant of the point-biserial correlation coefficient (PBCC) as a measure to characterise the introspective capacity of a classifier and apply it to investigate further the introspective capacity of boosting ? a well established, efficient machine learning framework commonly used in robotics. While recent evidence suggests that boosting is prone to providing overconfident classification output (i.e. it has a low introspective capacity), we investigate whether optimising this criterion directly leads to an improved introspective capacity. We show that with only a slight modification in the AdaBoost algorithm the resulting classifier becomes less confident when making incorrect predictions, rendering it significantly more useful when it comes to efficient robot decision making.


@INPROCEEDINGS { TriebelICRAWorkshop2013,
ADDRESS = { Karlsruhe, Germany },
AUTHOR = { Rudolph Triebel, Hugo Grimmett, Ingmar Posner },
BOOKTITLE = { Workshop. IEEE International Conference on Robotics and Automation (ICRA) },
MONTH = { May },
TITLE = { Confidence Boosting: Improving the Introspectiveness of a Boosted Classifier for Efficient Learning },
YEAR = { 2013 },
}