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حسابداری و مالی::
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, 2009) highlight that while lowering the verifiability threshold of losses facilitates timely creditor intervention, it also increases the incidence of costly ―false positives‖ caused by losses that are not indicative of future performance.
In these studies, asymmetrically decreasing the verifiability threshold for loss recognition facilitates timely creditor intervention by decreasing the incidence of false negative errors; however, it also increases the incidence of false positive errors triggered by noisy losses that are not indicative of the true state of the nature.
, costs of false positives) dominate the costs of continuation of a bad project (costs of false negatives), making more liberal accounting desirable.
‖ We manually filter false positive results from the search by reading the content returned by the searches.
―false negatives‖, it also increases the incidence of ―false positives‖ triggered by noisy losses that are
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برق و الکترونیک::
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Acronyms AC Auxiliary Classifier ACr Adjusted Cardinality ANN Artificial Neural Networks AV Adjusted Value CA Clustering Accuracy Card Cardinality CAS Classification Assessment Scores CD Concept Drift cda Concept Drift Amount CS Validity index presented by Chou and Su CSTR Continuous Stirred Tank Reactor DB Validity index presented by Davies and Bouldin DT Decision Trees DW Dynamic Window F False FD Fault Detection FDD Fault Detection and Diagnosis FN False Negative FP False Positive F1 Fault 1 F2 Fault 2 F3 Fault 3 FSM Feature Selection Method FW Fixed sliding Window IL Incremental Learning ILDW Incremental Learning Dynamic Window
If the sample is truly negative and it is classified as negative, it is a True Negative (TN), but if it is classified as positive, it is a False Positive (FP).
In which is TP and is FP.
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