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Articles

The total operating characteristic to measure diagnostic ability for multiple thresholds

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Pages 570-583 | Received 03 Sep 2013, Accepted 23 Oct 2013, Published online: 06 Jan 2014

References

  • Blough, D.S., 1967. Stimulus generalization as signal detection in pigeons. Science, 158 (3803), 940–941.
  • Bradley, A.P., 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30 (7), 1145–1159.
  • Cain, W.S., 1977. Differential sensitivity for smell: ‘noise’ at the nose. Science, 195 (4280), 796–798.
  • Camacho Olmedo, M.T., Paegelow, M., and Mas, J.F., 2013. Interest in intermediate soft-classified maps in land change model validation: suitability versus transition potential. International Journal of Geographical Information Science, 27 (12), 2343–2361. 
  • Carterette, E.D. and Jones, M.H., 1967. Visual and auditory information processing in children and adults. Science, 156 (3777), 986–988.
  • Chen, Y., et al., 2013. Modeling urban land-use dynamics in a fast developing city using the modified logistic cellular automaton with a patch-based simulation strategy. International Journal of Geographical Information Science, 28 (2), 234–255. 
  • Conway, T.M. and Wellen, C.C., 2011. Not developed yet? Alternative ways to include locations without changes in land use change models. International Journal of Geographical Information Science, 25 (10), 1613–1631.
  • Cook, N.R., 2007. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation, 115 (7), 928–935.
  • Dodd, L.E. and Pepe, M.S., 2003. Partial AUC estimation and regression. Biometrics, 59 (3), 614–623.
  • Eastman, J.R., Van Fossen, M., and Solorzano, L.A., 2005. Transition potential modeling for land cover change. In: D. Maguire, M. Batty, and M.F. Goodchild, eds. GIS, Spatial analysis and modeling. Redlands, CA: ESRI Press, 339–368.
  • Egan, J.P., 1975. Signal detection theory and ROC analysis. Series in cognition and perception. New York: Academic Press.
  • Fawcett, T., 2006. An introduction to ROC analysis. Pattern Recognition Letters, 27 (8), 861–874.
  • Fielding, A.H. and Bell, J.F., 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24 (1), 38–49.
  • Fleming, S.M., et al., 2010. Relating introspective accuracy to individual differences in brain structure. Science, 329 (5998), 1541–1543.
  • Gao, J. and Zhang, Y., 2012. Incorporating spectral data into logistic regression model to classify land cover: a case study in Mt. Qomolangma (Everest) National Nature Preserve. International Journal of Geographical Information Science, 26 (10), 1–18.
  • Gerçek, D., Toprak, V., and Strobl, J., 2011. Object-based classification of landforms based on their local geometry and geomorphometric context. International Journal of Geographical Information Science, 25 (6), 1011–1023.
  • Golicher, D., et al., 2012. Pseudo-absences, pseudo-models and pseudo-niches: pitfalls of model selection based on the area under the curve. International Journal of Geographical Information Science, 26 (11), 2049–2063.
  • Green, D.M. and Swets, J.A., 1966. Signal detection theory and psychophysics. New York: Wiley Press.
  • Hand, D.J. and Till, R.J., 2001. A simple generalization of the area under the ROC curve to multiple class classification problems. Machine Learning, 45 (2), 171–186.
  • Hanley, J.A. and McNeil, B.J., 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143 (1), 29–36.
  • Hirzel, A.H., et al., 2006. Evaluating the ability of habitat suitability models to predict species presences. Ecological Modeling, 199 (2), 142–152.
  • Jansen, L.J.M. and Veldkamp, T., 2011. Evaluation of the variation in semantic contents of class sets on modelling dynamics of land-use changes. International Journal of Geographical Information Science, 26 (4), 717–746.
  • Jeong, M.-H., et al., 2013. Decentralized and coordinate-free computation of critical points and surface networks in a discretized scalar field. International Journal of Geographical Information Science, 28 (1), 1–21. 
  • Jiang, Y.L., Metz, C.E., and Nishikawa, R.M., 1996. A receiver operating characteristic partial area index for highly sensitive diagnostic tests. Radiology, 201 (3), 745–750.
  • Johnson, D.S., et al., 2007. Genome-wide mapping of in vivo protein-DNA interactions. Science, 316 (5830), 1497–1502.
  • Johnson, J.M., et al., 2003. Genome-wide survey of human alternative pre-mRNA splicing with exon junction microarrays. Science, 302 (5653), 2141–2144.
  • Jolliffe, I.T. and Stephenson, D.B., 2003. Forecast verification a practitioner’s guide in atmospheric science. Chichester: Wiley.
  • Knaus, W.A., Wagner, D.P., and Lynn, J., 1991. Short-term mortality predictions for critically ill hospitalized adults: science and ethics. Science, 254 (5030), 389–394.
  • Kolb, M., Mas, J.-F., and Galicia, L., 2013. Evaluating drivers of land-use change and transition potential models in a complex landscape in Southern Mexico. International Journal of Geographical Information Science, 27 (9), 1804–1827. 
  • Leitão, P.J., Moreira, F., and Osborne, P.E., 2011. Effects of geographical data sampling bias on habitat models of species distributions: a case study with steppe birds in southern Portugal. International Journal of Geographical Information Science, 25 (3), 439–454.
  • Li, L., Wang, J., and Leung, H., 2010. Using spatial analysis and Bayesian network to model the vulnerability and make insurance pricing of catastrophic risk. International Journal of Geographical Information Science, 24 (12), 1759–1784.
  • Lin, Y., et al., 2010. Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, 25 (1), 65–87.
  • Lobo, J.M., Jimenez-Valverde, A., and Real, R., 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecology and Biogeography, 17 (2), 145–151.
  • Lusted, L.B., 1971. Signal detectability and medical decision-making. Science, 171 (3977), 1217–1219.
  • Manzo, G., et al., 2012. GIS techniques for regional-scale landslide susceptibility assessment: the Sicily (Italy) case study. International Journal of Geographical Information Science, 27 (7), 1433–1452.
  • Mas, J.-F., et al., 2013. A suite of tools for ROC analysis of spatial models. ISPRS International Journal of Geo-Information, 2 (3), 869–887.
  • Mateo Sánchez, M.C., Cushman, S.A., and Saura, S., 2013. Scale dependence in habitat selection: the case of the endangered brown bear (Ursus arctos) in the Cantabrian Range (NW Spain). International Journal of Geographical Information Science. doi:10.1080/13658816.2013.776684 
  • Miller, J. and Franklin, J., 2002. Modeling the distribution of four vegetation alliances using generalized linear models and classification trees with spatial dependence. Ecological Modeling, 157 (2), 227–247.
  • Nevin, J.A., 1965. Decision theory in studies of discrimination in animals. Science, 150 (3699), 1057.
  • Nuechterlein, K.H., Parasuraman, R., and Jiang, Q., 1983. Visual sustained attention: image degradation produces rapid sensitivity decrement over time. Science, 220 (4594), 327–329.
  • Overmars, K.P. and Verburg, P.H., 2005. Analysis of land use drivers at the watershed and household level: linking two paradigms at the Philippine forest fringe. International Journal of Geographical Information Science, 19 (2), 125–152.
  • Paegelow, M. and Olmedo, M.T.C., 2005. Possibilities and limits of prospective GIS land cover modelling – a compared case study: Garrotxes (France) and Alta Alpujarra Granadina (Spain). International Journal of Geographical Information Science, 19 (6), 697–722.
  • Pérez-Vega, A., Mas, J.-F., and Ligmann-Zielinska, A., 2012. Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest. Environmental Modelling & Software, 29 (1), 11–23.
  • Peters, J., et al., 2011. Synergy of very high resolution optical and radar data for object-based olive grove mapping. International Journal of Geographical Information Science, 25 (6), 971–989.
  • Peterson, A.T., Papes, M., and Soberón, J., 2008. Rethinking receiver operating characteristic analysis applications in ecological niche modeling. Ecological Modeling, 213 (1), 63–72.
  • Pontius, R.G. and Batchu, K., 2003. Using the relative operating characteristic to quantify certainty in prediction of location of land cover change in India. Transactions in GIS, 7 (4), 467–484.
  • Pontius, R.G. and Pacheco, P., 2004. Calibration and validation of a model of forest disturbance in the Western Ghats, India 1920–1990. GeoJournal, 61 (4), 325–334. 
  • Reed, A.V., 1973. Speed-accuracy trade-off in recognition memory. Science, 181 (4099), 574–576.
  • Saatchi, S., et al., 2008. Modeling distribution of Amazonian tree species and diversity using remote sensing measurements. Remote Sensing of Environment, 112 (5), 2000–2017.
  • Smith, E.L.D., et al., 1982. Color vision is altered during the suppression phase of binocular rivalry. Science, 218 (4574), 802–804.
  • Stephan, C., et al., 2003. Comparison of eight computer programs for receiver-operating characteristic analysis. Clinical Chemistry, 49 (3), 433–439.
  • Swets, J.A., 1961. Is there a sensory threshold? Science, 134 (3473), 168–177.
  • Swets, J.A., 1963. Information retrieval systems. Science, 141 (3577), 245–250.
  • Swets, J.A., 1973. The relative operating characteristic in psychology. Science, 182 (4116), 990–1000.
  • Swets, J.A., 1988. Measuring the accuracy of diagnostic systems. Science, 240 (4857), 1285–1293.
  • Swets, J.A., et al., 1979. Assessment of diagnostic technologies. Science, 205 (4408), 753–759.
  • Wang, J. and Mountrakis, G., 2010. Developing a multi-network urbanization model: a case study of urban growth in Denver, Colorado. International Journal of Geographical Information Science, 25 (2), 229–253.
  • Wang, N., et al., 2013. Comparative performance of logistic regression and survival analysis for detecting spatial predictors of land-use change. International Journal of Geographical Information Science, 27 (10), 1960–1982. 
  • Wiley, E.O., et al., 2003. Niche modeling and geographic range predictions in the marine environment using machine-learning algorithm. Oceanography, 16 (3), 120–127.

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