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The specificity describes how closely the index terms match the topics they represent [10] An index is said to be specific if the indexer uses parallel descriptors to the concept of the document and reflects the concepts precisely. [11] Specificity tends to increase with exhaustivity as the more terms you include, the narrower those terms will be.
[5] As discussed by critic Marshall Soules, medium specificity and media specific analysis are playing an important role in the emergence of new media art forms, such as Internet art. [6] Medium specificity suggests that a work of art can be said to be successful if it fulfills the promise contained in the medium used to bring the artwork into ...
When selecting terms for a controlled vocabulary, the designer has to consider the specificity of the term chosen, whether to use direct entry, inter consistency and stability of the language. Lastly the amount of pre-coordination (in which case the degree of enumeration versus synthesis becomes an issue) and post-coordination in the system is ...
The relationship between sensitivity and specificity, as well as the performance of the classifier, can be visualized and studied using the Receiver Operating Characteristic (ROC) curve. In theory, sensitivity and specificity are independent in the sense that it is possible to achieve 100% in both (such as in the red/blue ball example given above).
In 2005, Petrov, Dosher and Lu pointed out that perceptual learning may be explained in terms of the selection of which analyzers best perform the classification, even in simple discrimination tasks. They explain that the some part of the neural system responsible for particular decisions have specificity [ clarification needed ] , while low ...
In a diagnostic test, specificity is a measure of how well a test can identify true negatives. Specificity is also referred to as selectivity or true negative rate, and it is the percentage, or proportion, of the true negatives out of all the samples that do not have the condition (true negatives and false positives).
A classification model (classifier or diagnosis [7]) is a mapping of instances between certain classes/groups.Because the classifier or diagnosis result can be an arbitrary real value (continuous output), the classifier boundary between classes must be determined by a threshold value (for instance, to determine whether a person has hypertension based on a blood pressure measure).