Relevant bibliographies by topics / Misclassification

Academic literature on the topic 'Misclassification'

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Published: 4 June 2021
Last updated: 21 February 2023

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Journal articles on the topic "Misclassification"

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Liu, Zhenbing, Chunyang Gao, Huihua Yang, and Qijia He. "A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8035089.

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Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC) for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate, and negative class misclassification rate. In addition, we sampled test samples and training samples with different imbalance ratio and use F-measure, G-mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM.
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Sommer, Alfred. "Misclassification." Archives of Ophthalmology 126, no. 2 (February 1, 2008): 265. http://dx.doi.org/10.1001/archophthalmol.2007.73.

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Stenson, G. B., and R. A. Myers. "Accuracy of Pup Classifications and Its Effect on Population Estimates in the Hooded Seal (Cystophora cristata)." Canadian Journal of Fisheries and Aquatic Sciences 45, no. 4 (April 1, 1988): 715–19. http://dx.doi.org/10.1139/f88-086.

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Information from the classification of age-specific developmental stages has been used to adjust aerial survey estimates of pup production in a number of species of seals, including the hooded seal (Cystophora cristata). We test the assumption that hooded seal pups were accurately and consistently classified according to developmental stage and examine the consequences of misclassifications upon adjusted population estimates. We determined overall misclassification rates, the effect of survey height on classifications, and interobserver variability. At ice level, misclassifications rates were low (<3%). From an altitude of 30 m, newborn pups could not be classified correctly and misclassification rates for the two other stages of attended pups varied between 6.4 and 21.3%. There was no evidence of an overall bias in classifications or differences among observers although there was a significant interaction between day and stage. Individual pups appear to have been misclassified independently by each observer. Under actual survey conditions, observers classified a similar proportion of pups into each recognizable stage. The misclassification rates we observed did not significantly alter the previous population estimate. Methods for improving the current survey design include modifying classification criteria, providing observers with a period of on-ice training, and reducing the width of survey transects.
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Mangasarian, O. L. "Misclassification minimization." Journal of Global Optimization 5, no. 4 (December 1994): 309–23. http://dx.doi.org/10.1007/bf01096681.

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Pires, Magda Carvalho, and Roberto da Costa Quinino. "Repeated responses in misclassification binary regression: A Bayesian approach." Statistical Modelling 19, no. 4 (June 11, 2018): 412–43. http://dx.doi.org/10.1177/1471082x18773394.

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Binary regression models generally assume that the response variable is measured perfectly. However, in some situations, the outcome is subject to misclassification: a success may be erroneously classified as a failure or vice versa. Many methods, described in existing literature, have been developed to deal with misclassification, but we demonstrate that these methods may lead to serious inferential problems when only a single evaluation of the individual is taken. Thus, this study proposes to incorporate repeated and independent responses in misclassification binary regression models, considering the total number of successes obtained or even the simple majority classification. We use subjective prior distributions, as our conditional means prior, to evaluate and compare models. A data augmentation approach, Gibbs sampling, and Adaptive Rejection Metropolis Sampling are used for posterior inferences. Simulation studies suggested that repeated measures significantly improve the posterior estimates, in that these estimates are closer to those obtained in a case with no misclassifications with a lower standard deviation. Finally, we illustrate the usefulness of the new methodology with the analysis about defects in eyeglass lenses.
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Remaley, Alan T., Maureen L. Sampson, James M. DeLeo, Nancy A. Remaley, Beriuse D. Farsi, and Mark H. Zweig. "Prevalence-Value-Accuracy Plots: A New Method for Comparing Diagnostic Tests Based on Misclassification Costs." Clinical Chemistry 45, no. 7 (July 1, 1999): 934–41. http://dx.doi.org/10.1093/clinchem/45.7.934.

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Abstract The clinical accuracy of diagnostic tests commonly is assessed by ROC analysis. ROC plots, however, do not directly incorporate the effect of prevalence or the value of the possible test outcomes on test performance, which are two important factors in the practical utility of a diagnostic test. We describe a new graphical method, referred to as a prevalence-value-accuracy (PVA) plot analysis, which includes, in addition to accuracy, the effect of prevalence and the cost of misclassifications (false positives and false negatives) in the comparison of diagnostic test performance. PVA plots are contour plots that display the minimum cost attributable to misclassifications (z-axis) at various optimum decision thresholds over a range of possible values for prevalence (x-axis) and the unit cost ratio (UCR; y-axis), which is an index of the cost of a false-positive vs a false-negative test result. Another index based on the cost of misclassifications can be derived from PVA plots for the quantitative comparison of test performance. Depending on the region of the PVA plot that is used to calculate the misclassification cost index, it can potentially lead to a different interpretation than the ROC area index on the relative value of different tests. A PVA-threshold plot, which is a variation of a PVA plot, is also described for readily identifying the optimum decision threshold at any given prevalence and UCR. In summary, the advantages of PVA plot analysis are the following: (a) it directly incorporates the effect of prevalence and misclassification costs in the analysis of test performance; (b) it yields a quantitative index based on the costs of misclassifications for comparing diagnostic tests; (c) it provides a way to restrict the comparison of diagnostic test performance to a clinically relevant range of prevalence and UCR; and (d) it can be used to directly identify an optimum decision threshold based on prevalence and misclassification costs.
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Cheng, Simon, and Brian Powell. "Misclassification by Whom?" American Sociological Review 76, no. 2 (March 31, 2011): 347–55. http://dx.doi.org/10.1177/0003122411401249.

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GREENLAND, SANDER, and JAMES M. ROBINS. "CONFOUNDING AND MISCLASSIFICATION." American Journal of Epidemiology 122, no. 3 (September 1985): 495–506. http://dx.doi.org/10.1093/oxfordjournals.aje.a114131.

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Walker, Alexander M., and Stephan F. Lanes. "Misclassification of covariates." Statistics in Medicine 10, no. 8 (August 1991): 1181–96. http://dx.doi.org/10.1002/sim.4780100803.

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Chen, Chunhui, and O. L. Mangasarian. "Hybrid misclassification minimization." Advances in Computational Mathematics 5, no. 1 (December 1996): 127–36. http://dx.doi.org/10.1007/bf02124738.

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Dissertations / Theses on the topic "Misclassification"

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Liyanage, Nilani. "Misclassification bias in epidemiologic studies." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=23406.

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Misclassification of disease and/or exposure is a common and potentially serious problem in epidemiologic studies. The impact of misclassification may be profound and may invalidate results. Despite the fact that there have been a number of articles published on the significance of misclassification bias, many epidemiologic studies are carried out with little attention paid to this issue either in the design or the analysis. The goal of this thesis is to provide clarifications on issues surrounding misclassification of exposure in case-control studies. Specifically, the conditions under which misclassification is likely to occur, the potential impact on effect measures and how misclassification can be prevented through design and corrected for in the analysis are discussed in detail.
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Rosychuk, Rhonda Jean. "Accounting for misclassification in binary longitudinal data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape10/PQDD_0004/NQ44779.pdf.

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Rice, Kenneth Martin. "Models for misclassification of covariates in epidemiology." Thesis, University of Cambridge, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620230.

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Umar, Abdulkarim Mallam. "Stochastic SIR household epidemic model with misclassification." Thesis, University of Kent, 2016. https://kar.kent.ac.uk/62476/.

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Pole, Jason. "Quantifying misclassification in water disinfection by-product analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape4/PQDD_0021/MQ53021.pdf.

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Zhong, Mingyu. "AN ANALYSIS OF MISCLASSIFICATION RATES FOR DECISION TREES." Doctoral diss., University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2496.

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The decision tree is a well-known methodology for classification and regression. In this dissertation, we focus on the minimization of the misclassification rate for decision tree classifiers. We derive the necessary equations that provide the optimal tree prediction, the estimated risk of the tree's prediction, and the reliability of the tree's risk estimation. We carry out an extensive analysis of the application of Lidstone's law of succession for the estimation of the class probabilities. In contrast to existing research, we not only compute the expected values of the risks but also calculate the corresponding reliability of the risk (measured by standard deviations). We also provide an explicit expression of the k-norm estimation for the tree's misclassification rate that combines both the expected value and the reliability. Furthermore, our proposed and proven theorem on k-norm estimation suggests an efficient pruning algorithm that has a clear theoretical interpretation, is easily implemented, and does not require a validation set. Our experiments show that our proposed pruning algorithm produces accurate trees quickly that compares very favorably with two other well-known pruning algorithms, CCP of CART and EBP of C4.5. Finally, our work provides a deeper understanding of decision trees.
Ph.D.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Electrical Engineering PhD
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Hilliam, Rachel M. "Statistical discrimination with disease categories subject to misclassification." Thesis, De Montfort University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391859.

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Mayer, Cory A. "Improving Ultra-Wideband Localization by Detecting Radio Misclassification." DigitalCommons@CalPoly, 2018. https://digitalcommons.calpoly.edu/theses/1957.

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The Global Positioning System (GPS) and other satellite-based positioning systems are often a key component in applications requiring localization. However, accurate positioning in areas with poor GPS coverage, such as inside buildings and in dense cities, is in increasing demand for many modern applications. Fortunately, recent developments in ultra-wideband (UWB) radio technology have enabled precise positioning in places where it was not previously possible by utilizing multipath-resistant wide band pulses. Although ultra-wideband signals are less prone to multipath interference, it is still a bottleneck as increasingly ambitious projects continue to demand higher precision. Some UWB radios include on-board detection of multipath conditions, however the implementations are usually limited to basic condition checks. In order to address these shortcomings, We propose an application of machine learning to reliably detect non-line-of-sight conditions when the on-board radio classifier fails to recognize these conditions. Our solution includes a neural network classifier that is 99.98% accurate in a variety of environments.
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Pole, Jason. "Quantifying misclassification in water disinfection by-product analysis." Ottawa : National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.nlc-bnc.ca/obj/s4/f2/dsk1/tape4/PQDD%5F0021/MQ53021.pdf.

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Chu, Rong. "Bayesian adjustment for exposure misclassification in case-control studies." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/32108.

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Measurement error occurs frequently in observational studies investigating the relationship between exposure variables and the clinical outcome. Error-prone observations on the explanatory variable may lead to biased estimation and loss of power in detecting the impact of an exposure variable. The mechanism of measurement error, such as whether or in what way the quality of data is affected by the disease status, is seldom completely revealed to the investigators. This increases uncertainty in assessing the consequences of ignoring measurement error associated with observed data, and brings difficulties to adjustment for mismeasurement. In this study, we consider situations with a correctly specified binary response, and a misclassified binary exposure. We propose a solution to conduct Bayesian adjustment to correct for measurement error subject to varying differentiality, including the nondifferential misclassification, differential misclassification and nearly nondifferential misclassification. Our Bayesian model incorporates the randomness of exposure prevalences and misclassification parameters as prior distributions. The posterior model is constructed upon simulations generated by Gibbs sampler and Metropolis-Hastings algorithm. Internal validation data is utilized to insure the resulting model is identifiable. Meanwhile, we compare the Bayesian model with maximum likelihood estimation (MLE) and simulation extrapolation (MC-SIMEX) methods, using simulated datasets. The Bayesian and MLE models produce accurate and similar estimates for odds ratio in describing the association between the disease and exposure, when appropriate assumptions regarding the differentially of misclassification are made. The 90% credible or confidence intervals capture the truth approximately 90% of the time. A Bayesian method corresponding to nearly nondifferential prior belief compromises between the loss of efficiency and loss of accuracy associated with other prior assumptions. At the end, we look at two case-control studies with misclassified exposure variables, and aim to make valid inference about the effect parameter.
Science, Faculty of
Statistics, Department of
Graduate
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Books on the topic "Misclassification"

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Virginia. General Assembly. Joint Legislative Audit & Review Commission. Review of employee misclassification in Virginia. Richmond: Joint Legislative Audit and Review Commission, 2012.

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Institute, Pennsylvania Bar. Independent contractor v. employee: Repercussions of misclassification. [Mechanicsburg, Pa.]: Pennsylvania Bar Institute, 2011.

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Yi, Grace Y. Statistical Analysis with Measurement Error or Misclassification. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6640-0.

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Lee, Peter N. Misclassification of Smoking Habits and Passive Smoking. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73822-7.

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New York (State). Legislature. Assembly. Standing Committee on Labor. Public hearing on tax evasion through employee misclassification. New York]: En-De Court Reporting, 2010.

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Hilliam, Rachel M. Statistical discrimination with disease categories subject to misclassification. Leicester: De Montfort University, 2000.

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Tsai, Hsien-Tang. A single screening procedure using individual misclassification error. West Lafayette, Ind: Institute for Research in the Behavioral, Economic, and Management Sciences, Krannert Graduate School of Management, Purdue University, 1986.

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Hausman, Jerry A. Misclassification of a dependent variable in a discrete response setting. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1994.

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Pavlenko, Tatijana. Asymptotic behavior of the probabilities misclassification for discriminant functions with weighting. Toronto: University of Toronto, 1997.

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Lee, Peter N. Misclassification of smoking habits and passive smoking: A review of the evidence. Berlin: Springer-Verlag, 1988.

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Book chapters on the topic "Misclassification"

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Lash, Timothy L., Aliza K. Fink, and Matthew P. Fox. "Misclassification." In Statistics for Biology and Health, 79–108. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/b97920_6.

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Kestenbaum, Bryan. "Misclassification." In Epidemiology and Biostatistics, 27–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97433-0_8.

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Lash, Timothy L., Aliza K. Fink, and Matthew P. Fox. "Misclassification." In Statistics for Biology and Health, 79–108. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-87959-8_6.

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Kestenbaum, Bryan. "Misclassification." In Epidemiology and Biostatistics, 75–89. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-88433-2_8.

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Gustafson, Paul, and Sander Greenland. "Misclassification." In Handbook of Epidemiology, 639–58. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-0-387-09834-0_58.

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Fox, Matthew P., Richard F. MacLehose, and Timothy L. Lash. "Misclassification." In Statistics for Biology and Health, 141–95. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82673-4_6.

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Kestenbaum, Bryan. "Misclassification." In Epidemiology and Biostatistics, 113–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96644-1_9.

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Lee, Peter N. "Evidence on Misclassification." In Misclassification of Smoking Habits and Passive Smoking, 17–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73822-7_3.

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Bensman, David. "Misclassification of Independent Contractors." In Global Encyclopedia of Public Administration, Public Policy, and Governance, 4003–8. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-20928-9_2770.

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Rutkowski, Leszek, Maciej Jaworski, and Piotr Duda. "Misclassification Error Impurity Measure." In Studies in Big Data, 63–82. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13962-9_5.

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Conference papers on the topic "Misclassification"

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DePold, Hans R., Ravi Rajamani, William H. Morrison, and Krishna R. Pattipati. "A Unified Metric for Fault Detection and Isolation in Engines." In ASME Turbo Expo 2006: Power for Land, Sea, and Air. ASMEDC, 2006. http://dx.doi.org/10.1115/gt2006-91095.

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In this paper we make two key contributions. First, we formalize the effectiveness of fault detection and isolation (FDI) with a metric that globally considers the following: variance in engine parameter estimate residuals under normal conditions, costs of missed detections and false alarms, costs associated with misclassification of faults, fault frequencies and fault severities. Reducing the error variance increases the signal-to-noise ratio, thereby increasing the reliability and speed of fault-detection algorithms. Minimizing missed detections has enormous implications on operational safety, while minimizing false alarms and fault misclassifications has implications on downtime, asset management, cannot duplicates, and operational costs. This metric measures the trade off between reducing data error variances, between false and missed detects, and misclassification of faults. As a second contribution, we embed this metric in a systematic data-driven diagnostic optimization process for normative decisions on input parameter selection for residual generation, FDI methods and prediction/classification fusion techniques.
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Jiang, Yue, and Bojan Cukic. "Misclassification cost-sensitive fault prediction models." In the 5th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1540438.1540466.

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Xu, Ling, Bei Wang, Ling Liu, Mo Zhou, Shengping Liao, and Meng Yan. "Misclassification Cost-Sensitive Software Defect Prediction." In 2018 IEEE International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2018. http://dx.doi.org/10.1109/iri.2018.00047.

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White, Mason T., and SoYoung Jeon. "Using t-SNE to explore Misclassification." In 2019 IEEE MIT Undergraduate Research Technology Conference (URTC). IEEE, 2019. http://dx.doi.org/10.1109/urtc49097.2019.9660573.

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Keroglou, Christoforos, and Christoforos N. Hadjicostis. "Bound on the probability of HMM misclassification." In Automation (MED 2011). IEEE, 2011. http://dx.doi.org/10.1109/med.2011.5983107.

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Axelsson, Malin, Helena Backman, Lowie Vanfleteren, Caroline Stridsman, Linda Ekerljung, Berne Eriksson, Bright Nwaru, et al. "Underdiagnosis and misclassification of COPD in Sweden." In ERS International Congress 2020 abstracts. European Respiratory Society, 2020. http://dx.doi.org/10.1183/13993003.congress-2020.1395.

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Rokui, J. "Multistage building learning based on misclassification measure." In 9th International Conference on Artificial Neural Networks: ICANN '99. IEE, 1999. http://dx.doi.org/10.1049/cp:19991112.

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Mengle, Saket S. R., Nazli Goharian, and Alana Platt. "Discovering relationships among categories using misclassification information." In the 2008 ACM symposium. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1363686.1363899.

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Pruengkarn, Ratchakoon, Chun Che Fung, and Kok Wai Wong. "Using misclassification data to improve classification performance." In 2015 12th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2015. http://dx.doi.org/10.1109/ecticon.2015.7206950.

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Loga, M. "Hierarchical approach to water body status misclassification." In WATER POLLUTION 2012. Southampton, UK: WIT Press, 2012. http://dx.doi.org/10.2495/wp120091.

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Reports on the topic "Misclassification"

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Meyer, Bruce, and Nikolas Mittag. Misclassification in Binary Choice Models. Cambridge, MA: National Bureau of Economic Research, September 2014. http://dx.doi.org/10.3386/w20509.

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Lin, Zhongjian, and Yingyao Hu. Misclassification and the hidden silent rivalry. The IFS, February 2018. http://dx.doi.org/10.1920/wp.cem.2018.1218.

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Dvorkin, Maximiliano A. International trade and labor reallocation: misclassification errors, mobility, and switching costs. Federal Reserve Bank of St. Louis, 2021. http://dx.doi.org/10.20955/wp.2021.014.

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Chen, Huaizhi, Lauren Cohen, and Umit Gurun. Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds. Cambridge, MA: National Bureau of Economic Research, November 2019. http://dx.doi.org/10.3386/w26423.

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Searcy, Stephen W., and Kalman Peleg. Adaptive Sorting of Fresh Produce. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568747.bard.

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This project includes two main parts: Development of a “Selective Wavelength Imaging Sensor” and an “Adaptive Classifiery System” for adaptive imaging and sorting of agricultural products respectively. Three different technologies were investigated for building a selectable wavelength imaging sensor: diffraction gratings, tunable filters and linear variable filters. Each technology was analyzed and evaluated as the basis for implementing the adaptive sensor. Acousto optic tunable filters were found to be most suitable for the selective wavelength imaging sensor. Consequently, a selectable wavelength imaging sensor was constructed and tested using the selected technology. The sensor was tested and algorithms for multispectral image acquisition were developed. A high speed inspection system for fresh-market carrots was built and tested. It was shown that a combination of efficient parallel processing of a DSP and a PC based host CPU in conjunction with a hierarchical classification system, yielded an inspection system capable of handling 2 carrots per second with a classification accuracy of more than 90%. The adaptive sorting technique was extensively investigated and conclusively demonstrated to reduce misclassification rates in comparison to conventional non-adaptive sorting. The adaptive classifier algorithm was modeled and reduced to a series of modules that can be added to any existing produce sorting machine. A simulation of the entire process was created in Matlab using a graphical user interface technique to promote the accessibility of the difficult theoretical subjects. Typical Grade classifiers based on k-Nearest Neighbor techniques and linear discriminants were implemented. The sample histogram, estimating the cumulative distribution function (CDF), was chosen as a characterizing feature of prototype populations, whereby the Kolmogorov-Smirnov statistic was employed as a population classifier. Simulations were run on artificial data with two-dimensions, four populations and three classes. A quantitative analysis of the adaptive classifier's dependence on population separation, training set size, and stack length determined optimal values for the different parameters involved. The technique was also applied to a real produce sorting problem, e.g. an automatic machine for sorting dates by machine vision in an Israeli date packinghouse. Extensive simulations were run on actual sorting data of dates collected over a 4 month period. In all cases, the results showed a clear reduction in classification error by using the adaptive technique versus non-adaptive sorting.
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Saldanha, Ian J., Andrea C. Skelly, Kelly Vander Ley, Zhen Wang, Elise Berliner, Eric B. Bass, Beth Devine, et al. Inclusion of Nonrandomized Studies of Interventions in Systematic Reviews of Intervention Effectiveness: An Update. Agency for Healthcare Research and Quality (AHRQ), September 2022. http://dx.doi.org/10.23970/ahrqepcmethodsguidenrsi.

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Introduction: Nonrandomized studies of interventions (NRSIs) are observational or experimental studies of the effectiveness and/or harms of interventions, in which participants are not randomized to intervention groups. There is increasingly widespread recognition that advancements in the design and analysis of NRSIs allow NRSI evidence to have a much more prominent role in decision making, and not just as ancillary evidence to randomized controlled trials (RCTs). Objective: To guide decisions about inclusion of NRSIs for addressing the effects of interventions in systematic reviews (SRs), this chapter updates the 2010 guidance on inclusion of NRSIs in Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) SRs. The chapter focuses on considerations for decisions to include or exclude NRSIs in SRs. Methods: In November 2020, AHRQ convened a 20-member workgroup that comprised 13 members representing 8 of 9 AHRQ-appointed EPCs, 3 AHRQ representatives, 1 independent consultant with expertise in SRs, and 3 representatives of the AHRQ-appointed Scientific Resource Center. The workgroup received input from the full EPC Program regarding the process and specific issues through discussions at a virtual meeting and two online surveys regarding challenges with NRSI inclusion in SRs. One survey focused on current practices by EPCs regarding NRSI inclusion in ongoing and recently completed SRs. The other survey focused on the appropriateness, completeness, and usefulness of existing EPC Program methods guidance. The workgroup considered the virtual meeting and survey input when identifying aspects of the guidance that needed updating. The workgroup used an informal method for generating consensus about guidance. Disagreements were resolved through discussion. Results: We outline considerations for the inclusion of NRSIs in SRs of intervention effectiveness. We describe the strengths and limitations of RCTs, study design features and types of NRSIs, and key considerations for making decisions about inclusion of NRSIs (during the stages of topic scoping and refinement, SR team formation, protocol development, SR conduct, and SR reporting). We discuss how NRSIs may be applicable for the decisional dilemma being addressed in the SR, threats to the internal validity of NRSIs, as well as various data sources and advanced analytic methods that may be used in NRSIs. Finally, we outline an approach to incorporating NRSIs within an SR and key considerations for reporting. Conclusion: The main change from the previous guidance is the overall approach to decisions about inclusion of NRSIs in EPC SRs. Instead of recommending NRSI inclusion only if RCTs are insufficient to address the Key Question, this updated guidance handles NRSI evidence as a valuable source of information and lays out important considerations for decisions about the inclusion of NRSIs in SRs of intervention effectiveness. Different topics may require different decisions regarding NRSI inclusion. This guidance is intended to improve the utility of the final product to end-users. Inclusion of NRSIs will increase the scope, time, and resources needed to complete SRs, and NRSIs pose potential threats to validity, such as selection bias, confounding, and misclassification of interventions. Careful consideration must be given to both concerns.
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