Glossary of terms.

By Trish Perl. American Journal of Health-System Pharmacy, 2009

1,671 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3516119769657357b44e7212a89f2a9814264e4b

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Citations in the corpus

Breast cancer diagnosis using least square support vector machine (2007, background): “Confusion matrix A confusion matrix [19] contains information about actual and predicted classifications done by a classification system.”
Fame for sale: Efficient detection of fake Twitter followers (2015, background): “The meaning of each indicator is graphically highlighted by the matrix in Table 5 (called the confusion matrix ), where each column represents the instances in the predicted class, while each row represents the instances in the actual class [36]: In order to evaluate the application of each single r”
Consumer Credit Risk Models Via Machine-Learning Algorithms (2010, background): “See Kohavi and Provost (1998) for a formal definition of this and other machinelearning terminology.”
Classification of epileptiform EEG using a hybrid system based on decision tree classifier and fast Fourier transform (2007, methodology): “The advantage of this method is that we can independently choose the size of the each test and the number of trials [22].”
Predictive Association between Biotic Stress Traits and Eco-Geographic Data for Wheat and Barley Landraces (2011, background): “Thus, to assess the positive predictive performance for the identifi cation of resistant landraces, the so-called confusion matrices (Kohavi and Provost, 1998) for each trait mining experiment was fi rst collapsed to a two by two table.”
Support vector machines combined with feature selection for breast cancer diagnosis (2009, background): “A confusion matrix (Kohavi & Provost, 1998) contains information about actual and predicted classifications done by a classification system.”
Metrics for evaluating performance and uncertainty of Bayesian network models (2012, unknown): “Error rates are then calculated for false positives (Type I error, rejecting a true hypothesis), false negatives (Type II error, failing to reject a false hypothesis), and their sum, and are depicted in so-called confusion tables (Kohavi and Provost, 1998).”
Reinforcement Learning for Primary care e Appointment Scheduling (2017, unknown): “Such methods seek to approximate functions from data that can be used to predict outcomes to drive decision [30].”
Extended Taxonomy of Rule Extraction Techniques and Assessment of KDRuleEx (2012, background): “If a classifier does not classify all the instances then it would be important to know its performance on the set of instances for which it is confident to make prediction [26].”
Electronic Nose and Its Applications: A Survey (2019, unknown): “Some additional deep learning based examples include convolutional recurrent neural networks (consisting in both CNNs and recurrent neural networks (RNNs)) to perform fast gas recognition[114] and (deep) Boltzmann machines[115, 116] to overcome overlapping of odors captured by an EN in order to avoi”
Using data mining for bank direct marketing: an application of the CRISP-DM methodology (2011, background): “For instance, in classification tasks (to predict a discrete target), common metrics are the confusion matrix (Kohavi and Provost 1998) and the Receiver Operating Characteristic (ROC) curve (Fawcett 2005).”
Machine‐Learning‐Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index (2017, methodology): “26,27 These methods offer advantages for predictive clinical applications because they can be designed to yield stable predictions, 28,29 are able to perform variable selection as part of the model building process, 29 are ﬂ exible in handling predictor data favorable for electronic health record…”
Global climate risk index 2015 : who suffers most From extreme weather events? weather-related loss events in 2013 and 1994 to 2013. (2014, background): “In their latest reports, the IPCC (2019)88 focuses on the effect of climate change on, for example, the desertification and degradation of land. It suggests that climate change will accelerate several desertification processes and that, in the future, the risks of desertification will increase. This”