Robust binary hypothesis testing under contaminated likelihoods
By Dennis Wei, Kush R. Varshney. 2014
4 citations recorded by the source corpus.
In hypothesis testing, the phenomenon of label noise, in which hypothesis labels are switched at random, contaminates the likelihood functions. In this paper, we develop a new method to determine the decision rule when we do not have knowledge of the uncontaminated likelihoods and contamination probabilities, but only have knowledge of the contaminated likelihoods. In particular we pose a minimax optimization problem that finds a decision rule robust against this lack of knowledge. The method simplifies by application of linear programming theory. Motivation for this investigation is provided by problems encountered in workforce analytics.
Subfields: Statistics and Probability
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Citations in the corpus
- On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “However, since the prediction problem is to predict resignation in the next six months, our set of negati ve samples will necessarily include employees who should be labeled positively because they will be resigning soon [24] .”
- Data Science of the People , for the People , by the People : A Viewpoint on an Emerging Dichotomy (2015, background): “The main robustness considered in classification is against uncertainty in the class frequencies or relative costs of different types of errors [37, 17], but robustness against other uncertainties are also considered [49, 57].”