Confidence Boosting: Improving the Introspectiveness of a Boosted Classifier for E cient Learning
نویسندگان
چکیده
This paper concerns the recently introduced notion of introspective classification. We introduce a variant of the point-biserial correlation coe cient (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, e cient 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 e cient robot decision making.
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