One of the advantages of data envelopment analysis (DEA) is to determine benchmarks for inefficient decision making units (DMUs).
Composite indicator has been proven to be a tool for benchmarking and strategic decision making [9, 10, 11, 12], and can be used for monitoring maintenance and renewal in a capacity enhancement programme.
This class of models is central to optimal foraging theory (behavioral ecology) [2] and serves as benchmarks in economic decision making (maximum expected gain, maximum expected utility) [1].
Finally, some decision-makers emphasized the importance of (5) decision making tools, or benchmarking, to improve decision making.
Thus, the model is used here as a comparative benchmark to evaluate physician risk assessment and decision making, not as a replacement for physician decision making.
This study may provide key stakeholders, such as policy makers, scientists, manufacturers, purchasers, and clinicians, with benchmarks to assist their decision making.
An operative model diagnosis procedure was previously proposed in the companion paper, which can be readily used both for agile decision making on the architecture and implementation details of the machine learning algorithms, and for differential benchmarking among models.
Data envelopment analysis (DEA) has been a very popular method for measuring and benchmarking relative efficiency of each decision making units (DMUs) with multiple inputs and multiple outputs.
Yet, the linkage between the value of intangible assets and how to quantify or benchmark it is still tenuous, not to mention easily misunderstood by a layman for guiding better decision making.
The findings are as follows: (1) open issues and challenges to evaluation and benchmarking are emphasized; and (2) decision making using multiple criteria such as reliability, time complexity, and error rate within a dataset is used for evaluating and benchmarking real-time skin detectors to come up with solutions for future directions.
