Batch and online learning algorithms for nonconvex neyman-pearson classification
نویسندگان
چکیده
منابع مشابه
Neyman-Pearson classification algorithms and NP receiver operating characteristics
In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II ...
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In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (i.e., the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II err...
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Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i) its probability of type I error is below a pre-specifie...
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In the Neyman-Pearson (NP) classification paradigm, the goal is to learn a classifier from labeled training data such that the probability of a false negative is minimized while the probability of a false positive is below a user-specified level α ∈ (0, 1). This work addresses the question of how to evaluate and compare classifiers in the NP setting. Simply reporting false positives and false n...
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Motivated by problems of anomaly detection, this paper implements the Neyman-Pearson paradigm to deal with asymmetric errors in binary classification with a convex loss. Given a finite collection of classifiers, we combine them and obtain a new classifier that satisfies simultaneously the two following properties with high probability: (i), its probability of type I error is below a pre-specifi...
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ژورنال
عنوان ژورنال: ACM Transactions on Intelligent Systems and Technology
سال: 2011
ISSN: 2157-6904,2157-6912
DOI: 10.1145/1961189.1961200