NDLI: Using asymmetric distributions to improve text classifier probability estimates

Content Provider	ACM Digital Library
Author	Bennett, Paul N.
Abstract	Text classifiers that give probability estimates are more readily applicable in a variety of scenarios. For example, rather than choosing one set decision threshold, they can be used in a Bayesian risk model to issue a run-time decision which minimizes a user-specified cost function dynamically chosen at prediction time. However, the quality of the probability estimates is crucial. We review a variety of standard approaches to converting scores (and poor probability estimates) from text classifiers to high quality estimates and introduce new models motivated by the intuition that the empirical score distribution for the "extremely irrelevant", "hard to discriminate", and "obviously relevant" items are often significantly different. Finally, we analyze the experimental performance of these models over the outputs of two text classifiers. The analysis demonstrates that one of these models is theoretically attractive (introducing few new parameters while increasing flexibility), computationally efficient, and empirically preferable.
Starting Page	111
Ending Page	118
Page Count	8
File Format	PDF
ISBN	1581136463
DOI	10.1145/860435.860457
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2003-07-28
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cost-sensitive learning Classifier combination Active learning Text classification
Content Type	Text
Resource Type	Article

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Using asymmetric distributions to improve text classifier probability estimates