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Error rate control for classification rules in multiclass mixture models
[article]
2021
arXiv
pre-print
In the context of finite mixture models one considers the problem of classifying as many observations as possible in the classes of interest while controlling the classification error rate in these same classes. Similar to what is done in the framework of statistical test theory, different type I and type II-like classification error rates can be defined, along with their associated optimal rules, where optimality is defined as minimizing type II error rate while controlling type I error rate
arXiv:2109.14235v1
fatcat:anuzfnuf4fhu7g5ryierqtrwua