Bibliografie tematiche / Auc-Roc

Letteratura scientifica selezionata sul tema "Auc-Roc"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Auc-Roc":

Hong, Chong Sun, e So Yeon Choi. "ROC curve generalization and AUC". Journal of the Korean Data And Information Science Society 31, n. 4 (31 luglio 2020): 477–88. http://dx.doi.org/10.7465/jkdi.2020.31.4.477.

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Hong, Chong Sun, e Dae Soon Yang. "ROC curve and AUC for linear growth models". Journal of the Korean Data and Information Science Society 26, n. 6 (30 novembre 2015): 1367–75. http://dx.doi.org/10.7465/jkdi.2015.26.6.1367.

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Минин, А. С. "Бинаризация вероятностного прогноза методом ROC AUC". ТЕНДЕНЦИИ РАЗВИТИЯ НАУКИ И ОБРАЗОВАНИЯ 104, n. 14 (2023): 87–91. http://dx.doi.org/10.18411/trnio-12-2023-789.

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В работе проведено исследование влияния порога бинаризации вероятностного прогноза классификатора k-ближайших соседей на значение метрики ROC AUC. Путем варьирования порога бинаризации прогнозов и расчета ROC AUC выявлен оптимальный порог, при котором достигается максимальное значение метрики. Актуальность работы обусловлена широким практическим применением вероятностных классификаторов и необходимостью преобразования их непрерывных прогнозов в дискретные классы. Целью исследования является нахождение оптимального значения порога бинаризации для конкретного классификатора и набора данных на основе анализа зависимости метрики качества ROC AUC от величины порога. Полученные результаты могут быть использованы для настройки и оптимизации работы классификаторов, основанных на вероятностных прогнозах.

Krupinski, Elizabeth A. "Evaluating AI Clinically—It’s Not Just ROC AUC!" Radiology 298, n. 1 (gennaio 2021): 47–48. http://dx.doi.org/10.1148/radiol.2020203782.

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Mukhametshin, Rustam F., Olga P. Kovtun e Nadezhda S. Davydova. "Respiratory parameters as a predictor of hospital outcomes in newborns requiring medical evacuation". Russian Journal of Pediatric Surgery, Anesthesia and Intensive Care 12, n. 4 (19 gennaio 2023): 441–52. http://dx.doi.org/10.17816/psaic1292.

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BACKGROUND: Assessment of the clinical condition, prediction of risks and possible outcomes during the transfer of newborns remains an important part of the work of transport teams. Respiratory disorders remain a significant indication for transfer to medical organizations of a higher level of care. AIM: To study the predictive value of the parameters of respiratory support in newborns requiring medical evacuation for the outcomes of treatment. MATERIALS AND METHODS: The observational, cohort, retrospective study included data from neonatal to patients on ventilators (286 newborns) in the period from August 1, 2017 to December 31, 2018. Anamnesis parameters, intensive care volume, respiratory support settings, and assessments on scales (KSHONN, NTISS, TRIPS) were evaluated. Analyzed: 24-hours mortality, 7 days mortality, hospital mortality, air leakage syndrome. The assessment and comparison of the predictive value of the parameters in relation to the hospital outcomes was performed. RESULTS: The AUC ROC of SpO2/FiO2 for predicting 24-hours mortality was 0.984 [0.9661.000], which is significantly higher than the ROC of the saturation oxygenation index (AUC 0.972 [0.9490.995], p = 0.004). The area under the ROC of the 24-hours mortality on the TRIPS scale does not significantly differ from the saturation index of oxygenation (AUC 0.972 [0.9490.995], p = 0.113) and the mean airway pressure (AUC 0.943 [0.8841.000], p = 0.107). When predicting 7-day mortality, the saturation oxygenation index has AUC ROC (0.702 [0.5490.854]) significantly lower than AUC ROC for SpO2/FiO2 (0.762 [0.6380.887], p = 0.001). SpO2/FiO2 predicts total mortality with AUC ROC (0.759 [0.6770.841]). CONCLUSIONS: The mean airway pressure, saturation oxygenation index and SpO2/FiO2 have a high (AUC 0,9) predictive value for 24-hours mortality, while only SpO2/FiO2 reliably predicts total mortality with AUC ROC 0,7.

Muschelli, John. "ROC and AUC with a Binary Predictor: a Potentially Misleading Metric". Journal of Classification 37, n. 3 (23 dicembre 2019): 696–708. http://dx.doi.org/10.1007/s00357-019-09345-1.

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Khaidarov, A. G., A. I. Soloviev e D. A. Budko. "STUDY OF THE MOST EFFICIENT MODELS AND ATRIBUTION ALGORITHMS USING THE ROC AUC INDICATOR". Современные наукоемкие технологии (Modern High Technologies), n. 7 2022 (2022): 63–68. http://dx.doi.org/10.17513/snt.39234.

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García de Guadiana-Romualdo, Luis, María Dolores Albaladejo-Otón, Mario Berger, Enrique Jiménez-Santos, Roberto Jiménez-Sánchez, Patricia Esteban-Torrella, Sergio Rebollo-Acebes, Ana Hernando-Holgado, Alejandro Ortín-Freire e Javier Trujillo-Santos. "Prognostic performance of pancreatic stone protein in critically ill patients with sepsis". Biomarkers in Medicine 13, n. 17 (dicembre 2019): 1469–80. http://dx.doi.org/10.2217/bmm-2019-0174.

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Aim: To assess the prognostic value for 28-day mortality of PSP in critically ill patients with sepsis. Material & methods: 122 consecutive patients with sepsis were enrolled in this study. Blood samples were collected on admission and day 2. Results: On admission, the combination of PSP and lactate achieved an area under the receiver operating characteristic (AUC-ROC) of 0.796, similar to sequential organ failure assessment score alone (AUC-ROC: 0.826). On day 2, PSP was the biomarker with the highest performance (AUC-ROC: 0.844), although lower (p = 0.041) than sequential organ failure assessment score (AUC-ROC: 0.923). Conclusion: The combination of PSP and lactate and PSP alone, on day 2, have a good performance for prognosis of 28-day mortality and could help to identify patients who may benefit most from tailored intensive care unit management.

Sauka, Kudzai, Gun-Yoo Shin, Dong-Wook Kim e Myung-Mook Han. "Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning". Applied Sciences 12, n. 13 (25 giugno 2022): 6451. http://dx.doi.org/10.3390/app12136451.

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The ever-evolving cybersecurity environment has given rise to sophisticated adversaries who constantly explore new ways to attack cyberinfrastructure. Recently, the use of deep learning-based intrusion detection systems has been on the rise. This rise is due to deep neural networks (DNN) complexity and efficiency in making anomaly detection activities more accurate. However, the complexity of these models makes them black-box models, as they lack explainability and interpretability. Not only is the DNN perceived as a black-box model, but recent research evidence has also shown that they are vulnerable to adversarial attacks. This paper developed an adversarial robust and explainable network intrusion detection system based on deep learning by applying adversarial training and implementing explainable AI techniques. In our experiments with the NSL-KDD dataset, the PGD adversarial-trained model was a more robust model than DeepFool adversarial-trained and FGSM adversarial-trained models, with a ROC-AUC of 0.87. The FGSM attack did not affect the PGD adversarial-trained model’s ROC-AUC, while the DeepFool attack caused a minimal 9.20% reduction in PGD adversarial-trained model’s ROC-AUC. PGD attack caused a 15.12% reduction in the DeepFool adversarial-trained model’s ROC-AUC and a 12.79% reduction in FGSM trained model’s ROC-AUC.

Amala, R., e Sudesh Pundir. "ROC Curve and AUC for A Left-Truncated Sample from Rayleigh Distribution". American Journal of Mathematical and Management Sciences 34, n. 2 (31 dicembre 2014): 89–116. http://dx.doi.org/10.1080/01966324.2014.969461.

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Tesi sul tema "Auc-Roc":

Zheng, Shimin. "The ROC Curve and the Area under the Curve (AUC)". Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/139.

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Lu, Qing. "Methods for Designing and Forming Predictive Genetic Tests". Case Western Reserve University School of Graduate Studies / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=case1212197560.

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Yuan, Yan. "Empirical Likelihood-Based NonParametric Inference for the Difference between Two Partial AUCS". Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/math_theses/32.

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Abstract (sommario):

Compare the accuracy of two continuous-scale tests is increasing important when a new test is developed. The traditional approach that compares the entire areas under two Receiver Operating Characteristic (ROC) curves is not sensitive when two ROC curves cross each other. A better approach to compare the accuracy of two diagnostic tests is to compare the areas under two ROC curves (AUCs) in the interested specificity interval. In this thesis, we have proposed bootstrap and empirical likelihood (EL) approach for inference of the difference between two partial AUCs. The empirical likelihood ratio for the difference between two partial AUCs is defined and its limiting distribution is shown to be a scaled chi-square distribution. The EL based confidence intervals for the difference between two partial AUCs are obtained. Additionally we have conducted simulation studies to compare four proposed EL and bootstrap based intervals.

Huang, Xin. "Bootstrap and Empirical Likelihood-based Semi-parametric Inference for the Difference between Two Partial AUCs". Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/54.

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With new tests being developed and marketed, the comparison of the diagnostic accuracy of two continuous-scale diagnostic tests are of great importance. Comparing the partial areas under the receiver operating characteristic curves (pAUC) is an effective method to evaluate the accuracy of two diagnostic tests. In this thesis, we study the semi-parametric inference for the difference between two pAUCs. A normal approximation for the distribution of the difference between two pAUCs has been derived. The empirical likelihood ratio for the difference between two pAUCs is defined and its asymptotic distribution is shown to be a scaled chi-quare distribution. Bootstrap and empirical likelihood based inferential methods for the difference are proposed. We construct five confidence intervals for the difference between two pAUCs. Simulation studies are conducted to compare the finite sample performance of these intervals. We also use a real example as an application of our recommended intervals.

Sun, Fangfang. "Semi-parametric inference for the partial area under the ROC curve". unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-113213/.

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Thesis (M.S.)--Georgia State University, 2008.
Title from file title page. Gengsheng Qin, committee chair; Yu-Sheng Hsu, Yixin Fang, Yuanhui Xiao, committee members. Description based on contents viewed July 22, 2009. Includes bibliographical references (p. 29-30).

Zhou, Haochuan. "Statistical Inferences for the Youden Index". Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/5.

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In diagnostic test studies, one crucial task is to evaluate the diagnostic accuracy of a test. Currently, most studies focus on the Receiver Operating Characteristics Curve and the Area Under the Curve. On the other hand, the Youden index, widely applied in practice, is another comprehensive measurement for the performance of a diagnostic test. For a continuous-scale test classifying diseased and non-diseased groups, finding the Youden index of the test is equivalent to maximize the sum of sensitivity and specificity for all the possible values of the cut-point. This dissertation concentrates on statistical inferences for the Youden index. First, an auxiliary tool for the Youden index, called the diagnostic curve, is defined and used to evaluate the diagnostic test. Second, in the paired-design study to assess the diagnostic accuracy of two biomarkers, the difference in paired Youden indices frequently acts as an evaluation standard. We propose an exact confidence interval for the difference in paired Youden indices based on generalized pivotal quantities. A maximum likelihood estimate-based interval and a bootstrap-based interval are also included in the study. Third, for certain diseases, an intermediate level exists between diseased and non-diseased status. With such concern, we define the Youden index for three ordinal groups, propose the empirical estimate of the Youden index, study the asymptotic properties of the empirical Youden index estimate, and construct parametric and nonparametric confidence intervals for the Youden index. Finally, since covariates often affect the accuracy of a diagnostic test, therefore, we propose estimates for the Youden index with a covariate adjustment under heteroscedastic regression models for the test results. Asymptotic properties of the covariate-adjusted Youden index estimators are investigated under normal error and non-normal error assumptions.

Xu, Ping. "Evaluation of Repeated Biomarkers: Non-parametric Comparison of Areas under the Receiver Operating Curve Between Correlated Groups Using an Optimal Weighting Scheme". Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4261.

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Receiver Operating Characteristic (ROC) curves are often used to evaluate the prognostic performance of a continuous biomarker. In a previous research, a non-parametric ROC approach was introduced to compare two biomarkers with repeated measurements. An asymptotically normal statistic, which contains the subject-specific weights, was developed to estimate the areas under the ROC curve of biomarkers. Although two weighting schemes were suggested to be optimal when the within subject correlation is 1 or 0 by the previous study, the universal optimal weight was not determined. We modify this asymptotical statistic to compare AUCs between two correlated groups and propose a solution to weight optimization in non-parametric AUCs comparison to improve the efficiency of the estimator. It is demonstrated how the Lagrange multiplier can be used as a strategy for finding the weights which minimize the variance function subject to constraints. We show substantial gains of efficiency by using the novel weighting scheme when the correlation within group is high, the correlation between groups is high, and/or the disease incidence is small, which is the case for many longitudinal matched case-control studies. An illustrative example is presented to apply the proposed methodology to a thyroid function dataset. Simulation results suggest that the optimal weight performs well with a sample size as small as 50 per group.

Bitara, Matúš. "Srovnání heuristických a konvenčních statistických metod v data miningu". Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-400833.

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The thesis deals with the comparison of conventional and heuristic methods in data mining used for binary classification. In the theoretical part, four different models are described. Model classification is demonstrated on simple examples. In the practical part, models are compared on real data. This part also consists of data cleaning, outliers removal, two different transformations and dimension reduction. In the last part methods used to quality testing of models are described.

Khamesipour, Alireza. "IMPROVED GENE PAIR BIOMARKERS FOR MICROARRAY DATA CLASSIFICATION". OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1573.

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The Top Scoring Pair (TSP) classifier, based on the notion of relative ranking reversals in the expressions of two marker genes, has been proposed as a simple, accurate, and easily interpretable decision rule for classification and class prediction of gene expression profiles. We introduce the AUC-based TSP classifier, which is based on the Area Under the ROC (Receiver Operating Characteristic) Curve. The AUCTSP classifier works according to the same principle as TSP but differs from the latter in that the probabilities that determine the top scoring pair are computed based on the relative rankings of the two marker genes across all subjects as opposed to for each individual subject. Although the classification is still done on an individual subject basis, the generalization that the AUC-based probabilities provide during training yield an overall better and more stable classifier. Through extensive simulation results and case studies involving classification in ovarian, leukemia, colon, and breast and prostate cancers and diffuse large b-cell lymphoma, we show the superiority of the proposed approach in terms of improving classification accuracy, avoiding overfitting and being less prone to selecting non-informative pivot genes. The proposed AUCTSP is a simple yet reliable and robust rank-based classifier for gene expression classification. While the AUCTSP works by the same principle as TSP, its ability to determine the top scoring gene pair based on the relative rankings of two marker genes across {\em all} subjects as opposed to each individual subject results in significant performance gains in classification accuracy. In addition, the proposed method tends to avoid selection of non-informative (pivot) genes as members of the top-scoring pair.\\ We have also proposed the use of the AUC test statistic in order to reduce the computational cost of the TSP in selecting the most informative pair of genes for diagnosing a specific disease. We have proven the efficacy of our proposed method through case studies in ovarian, colon, leukemia, breast and prostate cancers and diffuse large b-cell lymphoma in selecting informative genes. We have compared the selected pairs, computational cost and running time and classification performance of a subset of differentially expressed genes selected based on the AUC probability with the original TSP in the aforementioned datasets. The reduce sized TSP has proven to dramatically reduce the computational cost and time complexity of selecting the top scoring pair of genes in comparison to the original TSP in all of the case studies without degrading the performance of the classifier. Using the AUC probability, we were able to reduce the computational cost and CPU running time of the TSP by 79\% and 84\% respectively on average in the tested case studies. In addition, the use of the AUC probability prior to applying the TSP tends to avoid the selection of genes that are not expressed (``pivot'' genes) due to the imposed condition. We have demonstrated through LOOCV and 5-fold cross validation that the reduce sized TSP and TSP have shown to perform approximately the same in terms of classification accuracy for smaller threshold values. In conclusion, we suggest the use of the AUC test statistic in reducing the size of the dataset for the extensions of the TSP method, e.g. the k-TSP and TST, in order to make these methods feasible and cost effective.

Wang, Binhuan. "Statistical Evaluation of Continuous-Scale Diagnostic Tests with Missing Data". Digital Archive @ GSU, 2012. http://digitalarchive.gsu.edu/math_diss/8.

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The receiver operating characteristic (ROC) curve methodology is the statistical methodology for assessment of the accuracy of diagnostics tests or bio-markers. Currently most widely used statistical methods for the inferences of ROC curves are complete-data based parametric, semi-parametric or nonparametric methods. However, these methods cannot be used in diagnostic applications with missing data. In practical situations, missing diagnostic data occur more commonly due to various reasons such as medical tests being too expensive, too time consuming or too invasive. This dissertation aims to develop new nonparametric statistical methods for evaluating the accuracy of diagnostic tests or biomarkers in the presence of missing data. Specifically, novel nonparametric statistical methods will be developed with different types of missing data for (i) the inference of the area under the ROC curve (AUC, which is a summary index for the diagnostic accuracy of the test) and (ii) the joint inference of the sensitivity and the specificity of a continuous-scale diagnostic test. In this dissertation, we will provide a general framework that combines the empirical likelihood and general estimation equations with nuisance parameters for the joint inferences of sensitivity and specificity with missing diagnostic data. The proposed methods will have sound theoretical properties. The theoretical development is challenging because the proposed profile log-empirical likelihood ratio statistics are not the standard sum of independent random variables. The new methods have the power of likelihood based approaches and jackknife method in ROC studies. Therefore, they are expected to be more robust, more accurate and less computationally intensive than existing methods in the evaluation of competing diagnostic tests.

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Capitoli di libri sul tema "Auc-Roc":

Klawonn, Frank, Frank Höppner e Sigrun May. "An Alternative to ROC and AUC Analysis of Classifiers". In Advances in Intelligent Data Analysis X, 210–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24800-9_21.

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Gentili, Elisabetta, Alice Bizzarri, Damiano Azzolini, Riccardo Zese e Fabrizio Riguzzi. "Regularization in Probabilistic Inductive Logic Programming". In Inductive Logic Programming, 16–29. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49299-0_2.

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AbstractProbabilistic Logic Programming combines uncertainty and logic-based languages. Liftable Probabilistic Logic Programs have been recently proposed to perform inference in a lifted way. LIFTCOVER is an algorithm used to perform parameter and structure learning of liftable probabilistic logic programs. In particular, it performs parameter learning via Expectation Maximization and LBFGS. In this paper, we present an updated version of LIFTCOVER, called LIFTCOVER+, in which regularization was added to improve the quality of the solutions and LBFGS was replaced by gradient descent. We tested LIFTCOVER+ on the same 12 datasets on which LIFTCOVER was tested and compared the performances in terms of AUC-ROC, AUC-PR, and execution times. Results show that in most cases Expectation Maximization with regularization improves the quality of the solutions.

Marcus, Pamela M. "Performance Measures". In Assessment of Cancer Screening, 23–38. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94577-0_3.

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AbstractPerformance measures reflect the link between cancer screening test results and cancer diagnoses. They measure the ability of cancer screening to lead to detection of cancer, and provide no evidence as to screening’s ability to reduce mortality. Performance measures are rarely considered sufficient evidence to implement cancer screening for the first time, though they have driven dissemination of tests that are thought to represent upgrades of established cancer screening tests. Chapter 3 presents the six key performance measures: sensitivity, specificity, positive predictive value, negative predictive value, false positive rate, and false negative rate. Receiver operating characteristic (ROC) curves and area under the curve (AUC), which are calculated from sensitivity and false positive rate, are presented as well. Additional aspects of performance measures, including the role of disease prevalence in cancer screening test performance (in particular, the impact on positive predictive value), are discussed.

Koncar, Philipp, e Denis Helic. "Employee Satisfaction in Online Reviews". In Lecture Notes in Computer Science, 152–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60975-7_12.

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Abstract Employee satisfaction impacts the efficiency of businesses as well as the lives of employees spending substantial amounts of their time at work. As such, employee satisfaction attracts a lot of attention from researchers. In particular, a lot of effort has been previously devoted to the question of how to positively influence employee satisfaction, for example, through granting benefits. In this paper, we start by empirically exploring a novel dataset comprising two million online employer reviews. Notably, we focus on the analysis of the influencing factors for employee satisfaction. In addition, we leverage our empirical insights to predict employee satisfaction and to assess the predictive strengths of individual factors. We train multiple prediction models and achieve accurate prediction performance (ROC AUC of best model $$=0.89$$ = 0.89 ). We find that the number of benefits received and employment status of reviewers are most predictive, while employee position has less predictive strengths for employee satisfaction. Our work complements existing studies and sheds light on the influencing factors for employee satisfaction expressed in online employer reviews. Employers may use these insights, for example, to correct for biases when assessing their reviews.

Feretzakis, Georgios, Aikaterini Sakagianni, Evangelos Loupelis, Dimitris Kalles, Vasileios Panteris, Lazaros Tzelves, Rea Chatzikyriakou et al. "Prediction of Hospitalization Using Machine Learning for Emergency Department Patients". In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220422.

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The objective of this study was to evaluate the predictive capability of five machine learning models regarding the admission or discharge of emergency department patients. A Random Forest classifier outperformed other models with respect to the area under the receiver operating characteristic curve (AUC ROC).

Kyparissidis Kokkinidis, Ilias, Evangelos Logaras, Emmanouil S. Rigas, Ioannis Tsakiridis, Themistoklis Dagklis, Antonis Billis e Panagiotis D. Bamidis. "Towards an Explainable AI-Based Tool to Predict Preterm Birth". In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230207.

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Preterm birth (PTB) is defined as delivery occurring before 37 weeks of gestation. In this paper, Artificial Intelligence (AI)-based predictive models are adapted to accurately estimate the probability of PTB. In doing so, pregnant women’ objective results and variables extracted from the screening procedure in combination with demographics, medical history, social history, and other medical data are used. A dataset consisting of 375 pregnant women is used and a number of alternative Machine Learning (ML) algorithms are applied to predict PTB. The ensemble voting model produced the best results across all performance metrics with an area under the curve (ROC-AUC) of approximately 0.84 and a precision–recall curve (PR-AUC) of approximately 0.73. An attempt to provide clinicians with an explanation of the prediction is performed to increase trustworthiness.

Sakagianni, Aikaterini, Christina Koufopoulou, Vassilios Verykios, Evangelos Loupelis, Dimitrios Kalles e Georgios Feretzakis. "Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning". In Caring is Sharing – Exploiting the Value in Data for Health and Innovation. IOS Press, 2023. http://dx.doi.org/10.3233/shti230200.

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Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to the intensive care unit, and also to estimate future demand for hospital beds, equipment, and staff. The present study examined demographic data, hematological and biochemical markers routinely measured in Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, in relation to the ICU outcome, during the second and third Covid-19 waves, from October 2020 until February 2022. In this dataset, we applied eight well-known classifiers of the caret package for machine learning of the R programming language, to evaluate their performance in forecasting ICU mortality. The best performance regarding area under the receiver operating characteristic curve (AUC-ROC) was observed with Random Forest (0.82), while k-nearest neighbors (k-NN) were the lowest performing machine learning algorithm (AUC-ROC: 0.59). However, in terms of sensitivity, XGB outperformed the other classifiers (max Sens: 0.7). The six most important predictors of mortality in the Random Forest model were serum urea, age, hemoglobin, C-reactive protein, platelets, and lymphocyte count.

Lakshmi Shree K. e Ashok Kumar R. "Global Events to Enhance Tourism". In Advances in Marketing, Customer Relationship Management, and E-Services, 66–86. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6591-2.ch005.

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Targeting tourist behavior at global events could be one of the interesting topics for business houses. Location Based Analytical - Business Intelligent (LBA-BIntelligent) frameworks predict targeted tourist behavior at global events. The organizing countries create new businesses and gain a competitive advantage from tourists. This framework streamlines marketing to the right people at the right time. This study focuses on implementing bagging and boosting ensemble approaches. These classifiers have been evaluated on various parameters such as accuracy, precision, recall, F-score, Kappa score, and ROC-AUC score. The classification results show that the bagging approach gives better results through all the evaluation metrics.

Sweetnich, Stephen R., e David R. Jacques. "Skin Detection With Small Unmanned Aerial Systems by Integration of Area Scan Multispectral Imagers and Factors Affecting Their Design and Operation". In Unmanned Aerial Vehicles, 215–34. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8365-3.ch009.

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Dismount skin detection from an aerial platform has posed challenges compared to ground-based platforms. A small, area scanning multispectral imager was constructed and tested on a Small Unmanned Aerial System (SUAS). Computer vision registration, stereo camera calibration, and geolocation from autopilot telemetry were utilized to design a dismount detection platform. The test expedient prototype was 2kg and exhibited skin detection performance similar to a larger line scan hyperspectral imager (HSI). Outdoor tests with a line scan HSI and the prototype resulted in an average 5.112% difference in Receiver Operating Characteristic (ROC) Area Under Curve (AUC). This research indicated that SUAS-based Spectral Imagers are capable tools in dismount detection protocols.

Figueirêdo, Ilan, Lílian Lefol Nani Guarieiro e Erick Giovani Sperandio Nascimento. "Multivariate Real Time Series Data Using Six Unsupervised Machine Learning Algorithms". In Anomaly Detection - Recent Advances, Issues and Challenges [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94944.

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The development of artificial intelligence (AI) algorithms for classification purpose of undesirable events has gained notoriety in the industrial world. Nevertheless, for AI algorithm training is necessary to have labeled data to identify the normal and anomalous operating conditions of the system. However, labeled data is scarce or nonexistent, as it requires a herculean effort to the specialists of labeling them. Thus, this chapter provides a comparison performance of six unsupervised Machine Learning (ML) algorithms to pattern recognition in multivariate time series data. The algorithms can identify patterns to assist in semiautomatic way the data annotating process for, subsequentially, leverage the training of AI supervised models. To verify the performance of the unsupervised ML algorithms to detect interest/anomaly pattern in real time series data, six algorithms were applied in following two identical cases (i) meteorological data from a hurricane season and (ii) monitoring data from dynamic machinery for predictive maintenance purposes. The performance evaluation was investigated with seven threshold indicators: accuracy, precision, recall, specificity, F1-Score, AUC-ROC and AUC-PRC. The results suggest that algorithms with multivariate approach can be successfully applied in the detection of anomalies in multivariate time series data.

Atti di convegni sul tema "Auc-Roc":

Hong, Shenda, Cao Xiao, Trong Nghia Hoang, Tengfei Ma, Hongyan Li e Jimeng Sun. "RDPD: Rich Data Helps Poor Data via Imitation". In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/817.

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In many situations, we need to build and deploy separate models in related environments with different data qualities. For example, an environment with strong observation equipments (e.g., intensive care units) often provides high-quality multi-modal data, which are acquired from multiple sensory devices and have rich-feature representations. On the other hand, an environment with poor observation equipment (e.g., at home) only provides low-quality, uni-modal data with poor-feature representations. To deploy a competitive model in a poor-data environment without requiring direct access to multi-modal data acquired from a rich-data environment, this paper develops and presents a knowledge distillation (KD) method (RDPD) to enhance a predictive model trained on poor data using knowledge distilled from a high-complexity model trained on rich, private data. We evaluated RDPD on three real-world datasets and shown that its distilled model consistently outperformed all baselines across all datasets, especially achieving the greatest performance improvement over a model trained only on low-quality data by 24.56% on PR-AUC and 12.21% on ROC-AUC, and over that of a state-of-the-art KD model by 5.91% on PR-AUC and 4.44% on ROC-AUC.

Shekter, Dylan H., e Frank W. Samuelson. "Efficiently calculating ROC curves, AUC, and uncertainty from 2AFC studies with finite samples". In Image Perception, Observer Performance, and Technology Assessment, a cura di Frank W. Samuelson e Sian Taylor-Phillips. SPIE, 2020. http://dx.doi.org/10.1117/12.2550601.

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Ferris, Michael H., Michael McLaughlin, Samuel Grieggs, Soundararajan Ezekiel, Erik Blasch, Mark Alford, Maria Cornacchia e Adnan Bubalo. "Using ROC curves and AUC to evaluate performance of no-reference image fusion metrics". In NAECON 2015 - IEEE National Aerospace and Electronics Conference. IEEE, 2015. http://dx.doi.org/10.1109/naecon.2015.7443034.

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Chaves, Rubens Marques, André Luis Debiaso Rossi e Luís Paulo Faina Garcia. "A Financial Distress Prediction using a Non-stationary Dataset". In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/eniac.2023.234013.

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Financial distress prediction (FDP) is crucial to companies, investors, and authorities. However, most FDP studies have been based on stationary models, disregarding important challenges present on financial distress data such as non-stationarity. Therefore, the lack of real-world datasets of economic-financial indicators organized in a timeline manner is a gap to be addressed. This study proposes a comprehensive dataset of 84 economic-financial indicators from the Brazilian Securities and Exchange Commission (CVM) organized in a non-stationary manner and validated by experiments using classification models. The results of the metrics AUC-ROC, AUC-PS, F1-Score and Gmean bring evidences that the dataset is suitable for FDP.

Rodrigues, Gustavo, e Diego Kreutz. "Modelo preditivo para classificação de risco de óbito de pacientes com COVID-19 utilizando dados abertos". In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbcas.2022.222494.

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Com o intuito de mitigar a subjetividade de políticas para acesso a leitos de UTI, propomos um preditor baseado em florestas aleatórias para classificação de risco de óbito de pacientes com COVID-19. O conjunto de dados abertos utilizados engloba mais de 600 mil pacientes reportados através do Painel Coronavírus RS. No conjunto de teste, o modelo classificou a chance de óbito com uma pontuação AUC-ROC de 0,97. Estes resultados evidenciam o potencial do preditor em auxiliar na tomada de decisão no ambiente hospitalar.

Calheiros, José, Lucas Amorim, Lucas Lima e Marcelo Oliveira. "Os efeitos da utilização de atributos perinodulares na classificação de nódulos pulmonares". In Anais Principais do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbcas.2020.11510.

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Atualmente grande parte dos sistemas CADx vem utilizando apenas descritores oriundos da região do nódulo pulmonar. Trabalhos recentes indicam que há uma interação significativa entre o nódulo pulmonar e seu entorno, o parênquima, entretanto essa região tem sido pouco utilizada para o processo de diagnóstico do câncer pulmonar. O objetivo deste trabalho foi investigar o desempenho de descritores de imagem extraídos das regiões do nódulo, borda e parênquima (atributos intranodulares e perinodulares), na identificação de seu potencial para malignidade. Neste trabalho foram avaliados 897 nódulos pulmonares com 121 descritores de imagem extraídos da região tumoral. Os descritores foram selecionados por algoritmo genético e a avaliação de desempenho foi feita através da área sob a curva ROC (AUC) com validação cruzada de 10 folds e 5 repetições. Nosso melhor modelo avaliado obteve AUC média de 0,916, acurácia de 84,26%, sensibilidade de 84,45% e especificidade de 83,84%. Os resultados obtidos sustentam que a utilização de atributos perinodulares melhoram efetivamente o desempenho de classificação de nódulos pulmonares.

Yunisa, Regina, e Freddy Haryanto. "Sensitivity and accuracy analysis of CT image in PRISM autocontouring using confusion matrix and ROC/AUC curve methods". In THE 5TH INTERNATIONAL CONFERENCE ON MATHEMATICS AND NATURAL SCIENCES. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4930656.

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Alam, S., O. Olabiyi, O. Odejide e A. Annamalai. "Energy detector's performance evaluation in a relay based cognitive radio network: Area under the ROC curve (AUC) approach". In 2011 IEEE Globecom Workshops. IEEE, 2011. http://dx.doi.org/10.1109/glocomw.2011.6162466.

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Amagada, P. U. "An Inferable Machine Learning Approach for Reservoir Lithology Characterization Using Drilling Data". In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/217485-stu.

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Abstract Reservoir lithology is a key factor in petroleum exploration and petrophysical calculations. It is of utmost importance as it serves as a foundation for reservoir characterization and formation evaluation. Accurate estimation of the reservoir permeability, porosity, and water saturation, is greatly dependent on accurate identification of the reservoir lithology. Ideally, the reservoir lithology is determined by obtaining physical samples of the reservoir. This process is however very expensive and time-consuming, hence the wide adoption of well log responses for identifying the reservoir lithology. Most Machine learning approaches are imminently built to render good classification, and some have been adapted to probability estimation. The purpose of this study is to demonstrate how machine learning can be used to estimate the probability of reservoir lithology with the use of drilling data. The drilling data used in this research is from the Volve oil field in Stavanger, Norway. The preprocessed data consisted of pump pressure, surface torque average, rotation per minute of drill bit, mudflow rate, total gas content, effective circulation density, pump stroke rate, lithology type, and weight on bit. The data was split into 80% for training and 20% for the test set. Feature selection was done using expert domain knowledge. The three lithology characteristics captured by the data include sandstone, claystone, and marl. Intelligent models are algorithms designed to learn from large volumes of data and draw valuable insights from them. Examples are neural networks, logistic regression, and Random Forest. In this study, we are primarily interested in probabilistic prediction rather than label classification or a deterministic prediction. The problem was treated as a probability estimation problem using logistic regression, Decision trees, and Random Forest models. Decision Trees are a type of supervised machine learning where the data is continuously split according to a certain parameter. Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. Random Forest is an ensemble learning method for classification and regression that operates by constructing multiple decision trees at training time. The probabilistic classifier predicts a probability distribution over a set of lithology classes using drilling data. The stratified k-fold cross validation technique was used for model comparison on the training data. The performance of models was evaluated using the metrics- accuracy score, the area under the receiver operating characteristic curve (AUC), precision, recall and f1 score. The AUC score was considered to be the best evaluation metric for the task. We relied on the receiver operating characteristic curve (ROC) and the area under the curve (AUC) to evaluate the performance of the models. The higher the AUC, the better the ability to distinguish between the lithology classes. The logistic regression, Decision trees, and Random Forest models achieved ROC AUC scores of 0.7547, 0.8747, and 0.9932 respectively. The results revealed that the Random Forest model outperformed the other models. The Random Forest model achieved a ROC AUC score of 98.59% on the test dataset indicating its capability to estimate the probability of having a reservoir lithology with a high confidence level. This study resulted in the application of machine learning techniques to develop models capable of estimating the probability of a reservoir lithology in the absence of a reservoir sample. The models were developed by fitting logistic regression, Decision trees, and Random Forest machine-learning algorithms to a drilling dataset. The results revealed that the models performed satisfactorily in estimating the probability of a reservoir lithology. The Random Forest model outperformed the other models. Therefore, in the absence of a reservoir sample, the probability of a reservoir lithology can be estimated using the model. These predictions can be used for compatibility tests between formation and bit, improved bit selection programs, and drilling rate optimization. The accurate predictions from the model will be very useful for drilling planning and bit optimization thereby reducing drilling costs. Lithology characterization based on drilling data is also important for real-time geosteering in the oil and gas industry.

F. Machado, Giovani, Luciana F. Almeida e Juan G. Lazo Lazo. "Técnicas de Aprendizado de Máquina para Previsão de Perdas Severas em Rochas Carbonáticas de Reservatórios Do Pré-Sal". In Congresso Brasileiro de Automática - 2020. sbabra, 2020. http://dx.doi.org/10.48011/asba.v2i1.1264.

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Este trabalho visa apresentar modelos de classificadores binários para auxiliar na determinação da ocorrência do fenômeno de perda de circulação na construção de poços submarinos do pré-sal da Bacia de Santos. O conhecimento prévio sobre a possibilidade de ocorrência do fenômeno, possibilita alocar sondas com a tecnologia adequada para a construção dos poços. Neste contexto, os sistemas de classificação baseados em aprendizado de máquina podem apoiar a tomada de decisão. Neste trabalho, são propostos classificadores baseados em algoritmos clássicos de aprendizado de máquina e os resultados são apresentados utilizando a Área Sob a Curva ROC (AUC) como métrica.

Rapporti di organizzazioni sul tema "Auc-Roc":

Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, gennaio 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.

Chen, Xiaole, Peng Wang, Yunquan Luo, Yi-Yu Lu, Wenjun Zhou, Mengdie Yang, Jian Chen, Zhi-Qiang Meng e Shi-Bing Su. Therapeutic Efficacy Evaluation and Underlying Mechanisms Prediction of Jianpi Liqi Decoction for Hepatocellular Carcinoma. Science Repository, settembre 2021. http://dx.doi.org/10.31487/j.jso.2021.02.04.sup.

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Objective: The aim of this study was to assess the therapeutic effects of Jianpi Liqi decoction (JPLQD) in hepatocellular carcinoma (HCC) and explore its underlying mechanisms. Methods: The characteristics and outcomes of HCC patients with intermediate stage B who underwent sequential conventional transcatheter arterial chemoembolization (cTACE) and radiofrequency ablation (RFA) only or in conjunction with JPLQD were analysed retrospectively. The plasma proteins were screened using label-free quantitative proteomics analysis. The effective mechanisms of JPLQD were predicted through network pharmacology approach and partially verified by ELISA. Results: Clinical research demonstrated that the Karnofsky Performance Status (KPS), traditional Chinese medicine (TCM) syndrome scores, neutropenia and bilirubin, median progression-free survival (PFS), and median overall survival (OS) in HCC patients treated with JPLQD were superior to those in patients not treated with JPLQD (all P<0.05). The analysis of network pharmacology, combined with proteomics, suggested that 52 compounds targeted 80 potential targets, which were involved in the regulation of multiple signaling pathways, especially affecting the apoptosis-related pathways including TNF, p53, PI3K-AKT, and MAPK. Plasma IGFBP3 and CA2 were significantly up-regulated in HCC patients with sequential cTACE and RFA therapy treated with JPLQD than those in patients not treated with JPLQD (P<0.001). The AUC of the IGFBP3 and CA2 panel, estimated using ROC analysis for JPLQD efficacy evaluation, was 0.867. Conclusion: These data suggested that JPLQD improves the quality of life, prolongs the overall survival, protects liver function in HCC patients, and exhibits an anticancer activity against HCC. IGFBP3 and CA2 panels may be potential therapeutic targets and indicators in the efficacy evaluation for JPLQD treatment, and the effective mechanisms involved in the regulation of multiple signaling pathways, possibly affected the regulation of apoptosis.