Comparing classifiers when the misallocation costs are uncertain

Adams, Niall M.; Hand, David J.

doi:10.1016/s0031-3203(98)00154-x

Cited by 153 publications

(133 citation statements)

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“…ROC analysis was developed to assess the performance of radar receivers in detecting targets, but it has since been adopted in various scientific fields (Adams and Hand 1999;Provost and Fawcett 2001). The area under the ROC curve [area under curve (AUC)] can be used as a metric to assess the overall quality of a model (Hanley and McNeil 1982): the larger the area, the better the performance of the model over the whole range of possible cutoffs.…”

Section: Cutoff-dependent Accuracy Statisticsmentioning

confidence: 99%

Recommendations for the quantitative analysis of landslide risk

Corominas

Westen

Frattini

et al. 2013

Bull Eng Geol Environ

693

666

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This paper presents recommended methodologies for the quantitative analysis of landslide hazard, vulnerability and risk at different spatial scales (site-specific, local, regional and national), as well as for the verification and validation of the results. The methodologies described focus on the evaluation of the probabilities of occurrence of different landslide types with certain characteristics. Methods used to determine the spatial distribution of landslide intensity, the characterisation of the elements at risk, the assessment of the potential degree of damage and the quantification of the vulnerability of the elements at risk, and those used to perform the quantitative risk analysis are also described. The paper is intended for use by scientists and practising engineers, geologists and other landslide experts.

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Section: Cutoff-dependent Accuracy Statisticsmentioning

confidence: 99%

Recommendations for the quantitative analysis of landslide risk

Corominas

Westen

Frattini

et al. 2013

Bull Eng Geol Environ

693

666

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“…Después siguieron correlaciones moderadamente elevadas con el El criterio de decisión óptimo para la clasificación de los pacientes a partir del BFNE depende de diversos factores, como son gravedad de la condición de interés, la prevalencia de dicha condición en la población, la disponibilidad de medidas correctivas para los individuos clasificados y el coste financiero, emocional, etc. que conllevan las falsas alarmas (Adams & Hand, 1999). A partir de los valores observados de especificidad y sensibilidad, para el BFNE el punto de corte óptimo es de 48.…”

Section: Propiedades Psicométricas Del Bfneunclassified

Escala de miedo a la evaluación negativa versión breve (BFNE) : propiedades psicométricas en muestras clínicas y de universitarios en méxico.

Bravo

Betanzos

Navarro

et al. 2015

Acta. Colomb. Psicol.

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ResumenEl objetivo del presente trabajo es analizar en muestras clínicas y de universitarios mexicanos, las propiedades psicométricas de la Escala de Miedo a la Evaluación Negativa versión breve (BFNE), así como estudiar su utilidad diagnóstica al establecer un punto de corte que discrimine entre fobia social y trastorno de ansiedad generalizada. Los participantes fueron estudiantes universitarios, sujetos con fobia social y personas con trastorno de ansiedad generalizada. Se llevaron a cabo las siguientes actividades: 1) Proporcionar la media y desviación estándar del BFNE, para cada una de las muestras. 2) Confirmar la estructura interna de la escala. 3) Evaluar la consistencia interna. 4) Analizar la confiabilidad test-retest. 5) Estudiar la validez convergente y divergente. 6) Realizar un análisis de curvas ROC, en donde se establezca un punto de corte que sea capaz de discriminar entre sujetos con fobia social de aquellos que padecen trastorno de ansiedad generalizada. Se concluye que la Escala de Miedo a la Evaluación Negativa versión breve (BFNE) es una medida válida y confiable, capaz de detectar con gran eficacia la presencia de fobia social en las muestras estudiadas. Palabras clave: fobia social, trastorno de ansiedad social, miedo a la evaluación negativa, psicometría, BFNE BRIEF VERSION OF THE FEAR OF NEGATIVE EVALUATION SCALE (BFNE): PSYCHOMETRIC PROPERTIES IN CLINICAL AND UNIVERSITY STUDENT SAMPLES IN MEXICO AbstractThe aim of the present study is to analyze the psychometric properties of the Brief version of the Fear of Negative Evaluation Scale (BNFE) in clinical and university student samples in Mexico, as well as to assess their diagnostic value by establishing a cut-off score that discriminates between social phobia and generalized anxiety disorder. The sample of participants consisted of university students, subjects with social phobia and people with generalized anxiety disorder. The following activities were carried out: 1) To obtain the BFNE mean score and standard deviation for each sample, 2) To confirm the internal structure of scale. 3) To assess the internal consistency. 4) To analyze the test-retest reliability; 5) To examine the convergent and divergent validity. 6) To establish, by means of ROC analysis, a cut-off score that is able to discriminate between subjects with social phobia and those with generalized anxiety disorder. It is possible to conclude that the Brief version of the Fear of Negative Evaluation Scale (BFNE) is a reliable and valid measure, and it is able to detect effectively the social phobia in the studied samples.

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“…ROC analysis is now an integral part of the evaluation of machine learning algorithms (Bradley, 1997). Whereas ROC curves are widely (and rightly so) considered useful, both theoretical and practical shortcomings of the AUC have been pointed out (Hilden, 1991;Adams & Hand, 1999;Bengio, Mariéthoz, & Keller, 2005;Webb & Ting, 2005;Lobo et al, 2008;Hand, 2009;Hanczar, Hua, Sima, Weinstein, Bittner, & Dougherty, 2010;Hand & Anagnostopoulos, 2013;Parker, 2013). A particular problem of the AUC is that it can be incoherent, in the sense that it assumes different cost distributions for different classifiers (Hand, 2009).…”

Section: Area Under the Roc Curve (Auc)mentioning

confidence: 99%

An Empirical Evaluation of Ranking Measures With Respect to Robustness to Noise

Berrar¹

2014

jair

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Ranking measures play an important role in model evaluation and selection. Using both synthetic and real-world data sets, we investigate how different types and levels of noise affect the area under the ROC curve (AUC), the area under the ROC convex hull, the scored AUC, the Kolmogorov-Smirnov statistic, and the H-measure. In our experiments, the AUC was, overall, the most robust among these measures, thereby reinvigorating it as a reliable metric despite its well-known deficiencies. This paper also introduces a novel ranking measure, which is remarkably robust to noise yet conceptually simple.

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Comparing classifiers when the misallocation costs are uncertain

Cited by 153 publications

References 0 publications

Recommendations for the quantitative analysis of landslide risk

Recommendations for the quantitative analysis of landslide risk

Escala de miedo a la evaluación negativa versión breve (BFNE) : propiedades psicométricas en muestras clínicas y de universitarios en méxico.

An Empirical Evaluation of Ranking Measures With Respect to Robustness to Noise

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