Having stated the probabilistic model for ordinal classification problems with monotonicity constraints, the concepts of lower approximations are extended to the stochastic case.
As a result, responsibility of finding errors in the classification problem was up to the entire community of researchers rather than just peer-reviewers alone.
Evaluation metrics for multi-label classification performance are inherently different from those used in multi-class (or binary) classification, due to the inherent differences of the classification problem.
Tax and other regulatory bodies also face classification problems in terms of how different kinds of employment status are or are not legally recognised in contracts.
In machine learning, multi-label classification and the strongly related problem of multi-output classification are variants of the classification problem where multiple target labels must be assigned to each instance.
