Relevant bibliographies by topics / Ranking and selection

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ranking and selection'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 January 2023

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Journal articles on the topic "Ranking and selection"

1

Görder, Bjürn, and Michael Kolonko. "Ranking and Selection." ACM Transactions on Modeling and Computer Simulation 29, no. 1 (2019): 1–24. http://dx.doi.org/10.1145/3241042.

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Klimasara, Edward, та Andrzej P. Wierzbicki. "Examples of Objectified Multiple Criteria Ranking in the Selection of Infrastructural Projects". Journal of Telecommunications and Information Technology 8 (28 грудня 2018): 18–25. http://dx.doi.org/10.26636/jtit.2018.126718.

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The paper addresses the issue of multiple criteria rankings of infrastructural projects (buildings, roads, etc.). Although the amount of literature devoted to this subject is considerable, all methods proposed produce subjective rankings, dependent on a direct or indirect definition of weighting coefficients applicable to subsequent evaluation criteria. Infrastructural projects are usually selected and approved collegially, however, by a group of decision makers with preferences that may potentially differ significantly. Therefore, an objectified ranking, independent from subjectively defined weighti
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Levshuk, D. G. "Ranking framework for portfolio selection." Economic Analysis: Theory and Practice 18, no. 11 (2019): 2172–86. http://dx.doi.org/10.24891/ea.18.11.2172.

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Rajiv, S., R. Pushpakumar, and S. Pradeep Kumar. "Cloud Service Ranking and Selection." Journal of Computational and Theoretical Nanoscience 15, no. 9 (2018): 2928–32. http://dx.doi.org/10.1166/jctn.2018.7568.

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Kukuła, Karol, and Lidia Luty. "Once More about the Selection Procedure for the Linear Ordering Method." Przegląd Statystyczny 64, no. 2 (2017): 163–76. http://dx.doi.org/10.5604/01.3001.0014.0789.

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In this paper once again the issue of selecting the method of linear ordering was analyzed as a response to proposed procedure presented in Kisielińska’s paper (2016). It was shown in two examples that the ranking depends on the method used, and the differences are significant. Underlined the need to apply several methods before the construction of the final ranking and eliminating the “outliers” rankings. The article proposes a procedure for their elimination. It is based on the arithmetic mean and deviation of average measures of rankings’ similarity obtained with the use of several linear o
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Chen, Yilang, and Anjan Bose. "Direct Ranking for Voltage Contingency Selection." IEEE Power Engineering Review 9, no. 11 (1989): 31–32. http://dx.doi.org/10.1109/mper.1989.4310366.

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Blondel, Mathieu, Akio Onogi, Hiroyoshi Iwata, and Naonori Ueda. "A Ranking Approach to Genomic Selection." PLOS ONE 10, no. 6 (2015): e0128570. http://dx.doi.org/10.1371/journal.pone.0128570.

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Peng, Yijie, Edwin K. P. Chong, Chun-Hung Chen, and Michael C. Fu. "Ranking and Selection as Stochastic Control." IEEE Transactions on Automatic Control 63, no. 8 (2018): 2359–73. http://dx.doi.org/10.1109/tac.2018.2797188.

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Mukhopadhyay, N., and T. K. S. Solanky. "Estimation after sequential selection and ranking." Metrika 45, no. 1 (1997): 95–106. http://dx.doi.org/10.1007/bf02717096.

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Chen, Y., and A. Bose. "Direct ranking for voltage contingency selection." IEEE Transactions on Power Systems 4, no. 4 (1989): 1335–44. http://dx.doi.org/10.1109/59.41683.

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Dissertations / Theses on the topic "Ranking and selection"

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Lo, Siu-ming, and 盧小皿. "Factor analysis for ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B30162464.

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Lo, Siu-ming. "Factor analysis for ranking data /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B20792967.

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Marshall, Williams S. IV. "Robust variance estimation for ranking and selection." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/24263.

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Qi, Fang, and 齊放. "Some topics in modeling ranking data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/209210.

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Many applications of analysis of ranking data arise from different fields of study, such as psychology, economics, and politics. Over the past decade, many ranking data models have been proposed. AdaBoost is proved to be a very successful technique to generate a stronger classifier from weak ones; it can be viewed as a forward stagewise additive modeling using the exponential loss function. Motivated by this, a new AdaBoost algorithm is developed for ranking data. Taking into consideration the ordinal structure of the ranking data, I propose measures based on the Spearman/Kendall distance to e
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Ahmed, E. "On some problems in ranking and selection procedures." Thesis, University of Strathclyde, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.382276.

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Latham, Andrew C. "Multiple-Instance Feature Ranking." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1440642294.

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林漢坤 and Hon-kwan Lam. "Analysis of ranking data with covariates." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31215476.

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Lam, Hon-kwan. "Analysis of ranking data with covariates /." Hong Kong : University of Hong Kong, 1998. http://sunzi.lib.hku.hk/hkuto/record.jsp?B19943313.

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Malone, Gwendolyn Joy. "Ranking and Selection Procedures for Bernoulli and Multinomial Data." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/7603.

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Ranking and Selection procedures have been designed to select the best system from a number of alternatives, where the best system is defined by the given problem. The primary focus of this thesis is on experiments where the data are from simulated systems. In simulation ranking and selection procedures, four classes of comparison problems are typically encountered. We focus on two of them: Bernoulli and multinomial selection. Therefore, we wish to select the best system from a number of simulated alternatives where the best system is defined as either the one with the largest probability of s
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Healey, Christopher M. "Advances in ranking and selection: variance estimation and constraints." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34768.

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In this thesis, we first show that the performance of ranking and selection (R&S) procedures in steady-state simulations depends highly on the quality of the variance estimates that are used. We study the performance of R&S procedures using three variance estimators --- overlapping area, overlapping Cramer--von Mises, and overlapping modified jackknifed Durbin--Watson estimators --- that show better long-run performance than other estimators previously used in conjunction with R&S procedures for steady-state simulations. We devote additional study to the development of the new overlapping mod
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Books on the topic "Ranking and selection"

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Meyer, C. D. (Carl Dean), ed. Who's #1?: The science of rating and ranking. Princeton University Press, 2012.

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Yu, Philip. L. H., author, ed. Statistical methods for ranking data. Springer, 2014.

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Rida, Laraki, ed. Majority judgment: Measuring, electing, and ranking. MIT Press, 2011.

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Mukhopadhyay, Nitis. Multistage selection and ranking procedures: Second-order asymptotics. M. Dekker, 1994.

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Srivastava, M. S. Bootstrap method in ranking and slippage problems. University of Toronto, Dept. of Statistics, 1985.

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Srivastava, M. S. Bootstrap method in ranking and slippage problems. University of Toronto, Dept. of statistics, 1986.

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Brüggemann, Rainer. Ranking and Prioritization for Multi-indicator Systems: Introduction to Partial Order Applications. Springer Science+Business Media, LLC, 2011.

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Asimptoticheskie svoĭstva lineĭnykh rangovykh statistik. Vychislitelʹnyĭ t͡sentr AN SSSR, 1986.

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Allerup, Peter. Statistical analysis of MADRS: A rating scale. Danish Institute for Educational Research, 1986.

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Balakrishnan, N., H. N. Nagaraja, and N. Kannan, eds. Advances in Ranking and Selection, Multiple Comparisons, and Reliability. Birkhäuser Boston, 2005. http://dx.doi.org/10.1007/b138793.

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Book chapters on the topic "Ranking and selection"

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Kim, Seong-Hee. "Statistical Ranking and Selection." In Encyclopedia of Operations Research and Management Science. Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1162.

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Hunter, Susan R., and Barry L. Nelson. "Parallel Ranking and Selection." In Simulation Foundations, Methods and Applications. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64182-9_12.

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Ruiz, Roberto, Jesús S. Aguilar-Ruiz, and José C. Riquelme. "Wrapper for Ranking Feature Selection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28651-6_56.

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning Approaches for Algorithm Selection I (Exploiting Rankings)." In Metalearning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_2.

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SummaryThis chapter discusses an approach to the problem of algorithm selection, which exploits the performance metadata of algorithms (workflows) on prior tasks to generate recommendations for a given target dataset. The recommendations are in the form of rankings of candidate algorithms. The methodology involves two phases. In the first one, rankings of algorithms/workflows are elaborated on the basis of historical performance data on different datasets. These are subsequently aggregated into a single ranking (e.g. average ranking). In the second phase, the average ranking is used to schedule tests on the target dataset with the objective of identifying the best performing algorithm. This approach requires that an appropriate evaluation measure, such as accuracy, is set beforehand. In this chapter we also describe a method that builds this ranking based on a combination of accuracy and runtime, yielding good anytime performance. While this approach is rather simple, it can still provide good recommendations to the user. Although the examples in this chapter are from the classification domain, this approach can be applied to other tasks besides algorithm selection, namely hyperparameter optimization (HPO), as well as the combined algorithm selection and hyperparameter optimization (CASH) problem. As this approach works with discrete data, continuous hyperparameters need to be discretized first.
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Stańczyk, Urszula. "Ranking-Based Rule Classifier Optimisation." In Advances in Feature Selection for Data and Pattern Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67588-6_7.

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Hanselle, Jonas, Alexander Tornede, Marcel Wever, and Eyke Hüllermeier. "Hybrid Ranking and Regression for Algorithm Selection." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58285-2_5.

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Esawi, A. M. K., and M. F. Ashby. "Cost-Based Ranking for Manufacturing Process Selection." In Integrated Design and Manufacturing in Mechanical Engineering ’98. Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-015-9198-0_74.

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Chen, Chun-Hung, Stephen E. Chick, Loo Hay Lee, and Nugroho A. Pujowidianto. "Ranking and Selection: Efficient Simulation Budget Allocation." In Handbook of Simulation Optimization. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1384-8_3.

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Brazdil, Pavel, Jan N. van Rijn, Carlos Soares, and Joaquin Vanschoren. "Metalearning Approaches for Algorithm Selection II." In Metalearning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-67024-5_5.

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SummaryThis chapter discusses different types of metalearning models, including regression, classification and relative performance models. Regression models use a suitable regression algorithm, which is trained on the metadata and used to predict the performance of given base-level algorithms. The predictions can in turn be used to order the base-level algorithms and hence identify the best one. These models also play an important role in the search for the potentially best hyperparameter configuration discussed in the next chapter. Classification models identify which base-level algorithms are applicable or non-applicable to the target classification task. Probabilistic classifiers can be used to construct a ranking of potentially useful alternatives. Relative performance models exploit information regarding the relative performance of base-level models, which can be either in the form of rankings or pairwise comparisons. This chapter discusses various methods that use this information in the search for the potentially best algorithm for the target task.
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Blachnik, Marcin, Włodzisław Duch, and Tomasz Maszczyk. "Feature Ranking Methods Used for Selection of Prototypes." In Artificial Neural Networks and Machine Learning – ICANN 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33266-1_37.

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Conference papers on the topic "Ranking and selection"

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Goldsman, David. "Ranking and selection tutorial." In the 19th conference. ACM Press, 1987. http://dx.doi.org/10.1145/318371.318383.

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Geng, Xiubo, Tie-Yan Liu, Tao Qin, and Hang Li. "Feature selection for ranking." In the 30th annual international ACM SIGIR conference. ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277811.

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Lai, Hanjiang, Yong Tang, HaiXia Luo, and Yan Pan. "Greedy feature selection for ranking." In 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2011. http://dx.doi.org/10.1109/cscwd.2011.5960053.

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Shen, Haihui, L. Jeff Hong, and Xiaowei Zhang. "Ranking and selection with covariates." In 2017 Winter Simulation Conference (WSC). IEEE, 2017. http://dx.doi.org/10.1109/wsc.2017.8247946.

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Lee, Soonhui, and Barry L. Nelson. "Bootstrap ranking & selection revisited." In 2014 Winter Simulation Conference - (WSC 2014). IEEE, 2014. http://dx.doi.org/10.1109/wsc.2014.7020212.

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Goldsman, David, Barry L. Nelson, Tracy Opicka, and A. B. Pritsker. "A ranking and selection project." In the 31st conference. ACM Press, 1999. http://dx.doi.org/10.1145/324138.324165.

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Hua, Guichun, Min Zhang, Yiqun Liu, Shaoping Ma, and Liyun Ru. "Hierarchical feature selection for ranking." In the 19th international conference. ACM Press, 2010. http://dx.doi.org/10.1145/1772690.1772830.

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Seong-Hee Kim and Barry L. Nelson. "Recent advances in ranking and selection." In 2007 Winter Simulation Conference. IEEE, 2007. http://dx.doi.org/10.1109/wsc.2007.4419598.

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Batur, Demet, and F. Fred Choobineh. "Mean-variance based ranking and selection." In 2010 Winter Simulation Conference - (WSC 2010). IEEE, 2010. http://dx.doi.org/10.1109/wsc.2010.5679076.

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Ryzhov, Ilya O., Boris Defourny, and Warren B. Powell. "Ranking and selection meets robust optimization." In 2012 Winter Simulation Conference - (WSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/wsc.2012.6465209.

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Reports on the topic "Ranking and selection"

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Gupta, Shabti S., and S. Panchapakesan. On Sequential Ranking and Selection Procedures. Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada199689.

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Gupta, Shanti S., and Lii-Yuh Leu. On a Classification Problem: Ranking and Selection Approach. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada211627.

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Gupta, Shanti S. Simultaneous Inference, and Ranking Selection Procedure: Bayes and Empirical Bayes Approach. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada391935.

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Oldenburg, Curtis M. Health, Safety, and Environmental Screening and Ranking Frameworkfor Geologic CO2 Storage Site Selection. Office of Scientific and Technical Information (OSTI), 2005. http://dx.doi.org/10.2172/885235.

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Ghasemi, H., and T. Allen. Selection and ranking of groundmotion models for the 2018 National Seismic Hazard Assessment of Australia: summary of ground-motion data, methodology and outcomes. Geoscience Australia, 2018. http://dx.doi.org/10.11636/record.2018.029.

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Shapovalov, Yevhenii B., Viktor B. Shapovalov, and Vladimir I. Zaselskiy. TODOS as digital science-support environment to provide STEM-education. [б. в.], 2019. http://dx.doi.org/10.31812/123456789/3250.

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The amount of scientific information has been growing exponentially. It became more complicated to process and systemize this amount of unstructured data. The approach to systematization of scientific information based on the ontological IT platform Transdisciplinary Ontological Dialogs of Object-Oriented Systems (TODOS) has many benefits. It has been proposed to select semantic characteristics of each work for their further introduction into the IT platform TODOS. An ontological graph with a ranking function for previous scientific research and for a system of selection of journals has been w
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Sulfredge, CD. Scaling of Thermal-Hydraulic Experiments for a Space Rankine Cycle and Selection of a Preconceptual Scaled Experiment Design. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/885976.

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Monks, James, and Ronald Ehrenberg. The Impact of US News and World Report College Rankings on Admission Outcomes and Pricing Decisions at Selective Private Institutions. National Bureau of Economic Research, 1999. http://dx.doi.org/10.3386/w7227.

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Berkhout, Emilie, Goldy Dharmawan, Amanda Beatty, Daniel Suryadarma, and Menno Pradhan. Who Benefits and Loses from Large Changes to Student Composition? Assessing Impacts of Lowering School Admissions Standards in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/094.

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We study the effects of an admission policy change that caused a massive shift in student composition in public and private junior secondary schools in Yogyakarta, Indonesia. In 2018, the primary criterion for admission into Yogyakarta’s 16 preferred, free public schools (grades 7-9) changed from a grade 6 exam score ranking to a neighborhood-to-school distance ranking. This policy change resulted in a decline in average grade 6 scores in public schools by 0.4 standard deviations (s.d.) and a 0.4 s.d. increase in private schools. We assessed learning impacts caused by the changed student compo
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Berkhout, Emilie, Goldy Dharmawan, Amanda Beatty, Daniel Suryadarma, and Menno Pradhan. Who Benefits and Loses from Large Changes to Student Composition? Assessing Impacts of Lowering School Admissions Standards in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-risewp_2022/094.

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We study the effects of an admission policy change that caused a massive shift in student composition in public and private junior secondary schools in Yogyakarta, Indonesia. In 2018, the primary criterion for admission into Yogyakarta’s 16 preferred, free public schools (grades 7-9) changed from a grade 6 exam score ranking to a neighborhood-to-school distance ranking. This policy change resulted in a decline in average grade 6 scores in public schools by 0.4 standard deviations (s.d.) and a 0.4 s.d. increase in private schools. We assessed learning impacts caused by the changed student compo
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