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Journal articles on the topic 'Two stage selection'

Author: Grafiati

Published: 8 September 2023

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1

Yevseyeva, Iryna, Vitor Basto Fernandes, Aad van Moorsel, Helge Janicke, and Michael Emmerich. "Two-stage Security Controls Selection." Procedia Computer Science 100 (2016): 971–78. http://dx.doi.org/10.1016/j.procs.2016.09.261.

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2

Xu, Ke, Crystal Maung, Hiromasa Arai, and Haim Schweitzer. "Two-Stage Feature Selection with Unsupervised Second Stage." International Journal on Artificial Intelligence Tools 27, no. 07 (November 2018): 1860014. http://dx.doi.org/10.1142/s021821301860014x.

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Feature selection is a common dimensionality reduction technique of fundamental importance in big data. A common approach for reducing the running time of feature selection is to perform it in two stages. In the first stage a fast and simple filter is applied to select good candidates. The number of candidates is further reduced in the second stage by an accurate algorithm that may run significantly slower. There are two main variants of feature selection: unsupervised and supervised. In the supervised variant features are selected for predicting labels, while the unsupervised variant does not use labels at all. We describe a general framework that can use an arbitrary off-the-shelf unsupervised algorithm for the second stage. The algorithm is applied to the selection obtained in the first stage weighted appropriately. Our main technical result is a method for calculating weights for the columns that need to be selected in the second stage. We show that these weights can be computed as the solution to a constrained quadratic optimization problem. The solution is deterministic, and improves on previously published studies that use probabilistic ideas to compute similar weights. To the best of our knowledge our approach is the first technique for converting a supervised feature selection problem into an unsupervised problem. Complexity analysis shows that the proposed technique is very fast, can be implemented in a single pass over the data, and can take advantage of data sparsity. Experimental results show that the accuracy of the proposed method is comparable to that of much slower techniques.

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3

HATTORI, Tetsuya, and Hironari MIYAZAWA. "ACCURACY OF TWO-STAGE SELECTION METHOD." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 19, no. 2 (1992): 14–23. http://dx.doi.org/10.2333/jbhmk.19.2_14.

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4

Lam, K. "An improved two-stage selection procedure." Communications in Statistics - Simulation and Computation 17, no. 3 (January 1988): 995–1006. http://dx.doi.org/10.1080/03610918808812708.

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5

Sudbury, Aidan. "Two-stage testing using selection schemes." Statistics in Medicine 29, no. 21 (August 26, 2010): 2194–99. http://dx.doi.org/10.1002/sim.3965.

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6

Taboga, Marco. "Portfolio selection with two-stage preferences." Finance Research Letters 2, no. 3 (September 2005): 152–64. http://dx.doi.org/10.1016/j.frl.2005.06.003.

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7

Bécu, Jean-Michel, Yves Grandvalet, Christophe Ambroise, and Cyril Dalmasso. "Beyond support in two-stage variable selection." Statistics and Computing 27, no. 1 (November 20, 2015): 169–79. http://dx.doi.org/10.1007/s11222-015-9614-1.

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8

Jack Chen, E. "A revisit of two-stage selection procedures." European Journal of Operational Research 210, no. 2 (April 2011): 281–86. http://dx.doi.org/10.1016/j.ejor.2010.08.033.

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9

Simmonds, N. W. "Two-stage selection strategy in plant breeding." Heredity 55, no. 3 (December 1985): 393–99. http://dx.doi.org/10.1038/hdy.1985.123.

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10

Kasperski, Adam, and Paweł Zieliński. "Robust recoverable and two-stage selection problems." Discrete Applied Mathematics 233 (December 2017): 52–64. http://dx.doi.org/10.1016/j.dam.2017.08.014.

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11

Liu, Aiyi, Chengqing Wu, and Kai F. Yu. "Two-stage procedures for selecting the best diagnostic biomarkers." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1874 (April 11, 2008): 2293–99. http://dx.doi.org/10.1098/rsta.2008.0032.

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Considered in the paper is the problem of selecting a diagnostic biomarker that has the highest classification rate among several candidate markers with dichotomous outcomes. The probability of correct selection depends on a number of nuisance parameters from the joint distribution of the biomarkers and thus can be substantially affected if these nuisance parameters are misspecified. A two-stage procedure is proposed to compute the needed sample size that achieves the desired level of correct selection, as so confirmed by simulation results.

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12

Schulz-Streeck, Torben, Joseph O. Ogutu, and Hans-Peter Piepho. "Comparisons of single-stage and two-stage approaches to genomic selection." Theoretical and Applied Genetics 126, no. 1 (August 19, 2012): 69–82. http://dx.doi.org/10.1007/s00122-012-1960-1.

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13

Kamala, R. F., and P. R. J. Thangaiah. "A Novel Two-Stage Selection of Feature Subsets in Machine Learning." Engineering, Technology & Applied Science Research 9, no. 3 (June 8, 2019): 4169–75. http://dx.doi.org/10.48084/etasr.2735.

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In feature subset selection the variable selection procedure selects a subset of the most relevant features. Filter and wrapper methods are categories of variable selection methods. Feature subsets are similar to data pre-processing and are applied to reduce feature dimensions in a very large dataset. In this paper, in order to deal with this kind of problems, the selection of feature subset methods depending on the fitness evaluation of the classifier is introduced to alleviate the classification task and to progress the classification performance. To curtail the dimensions of the feature space, a novel approach for selecting optimal features on two-stage selection of feature subsets (TSFS) method is done, both theoretically and experimentally. The results of this method include improvements in the performance measures like efficiency, accuracy, and scalability of machine learning algorithms. Comparison of the proposed method is made with known relevant methods using benchmark databases. The proposed method performs better than the earlier hybrid feature selection methodologies discussed in relevant works, regarding classifiers’ accuracy and error.

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14

Campo, J. L., and M. B. de la Fuente. "Efficiency of Two-Stage Selection Indices in Tribolium." Journal of Heredity 82, no. 3 (May 1, 1991): 228–32. http://dx.doi.org/10.1093/oxfordjournals.jhered.a111070.

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15

Schulman, By N. F., M. J. De Vries, and M. R. Dentine. "Linkage disequilibrium in two-stage marker-assisted selection." Journal of Animal Breeding and Genetics 116, no. 2 (April 1999): 99–110. http://dx.doi.org/10.1046/j.1439-0388.1999.00183.x.

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16

Fei, Xin, Nalân Gülpınar, and Jürgen Branke. "Efficient solution selection for two-stage stochastic programs." European Journal of Operational Research 277, no. 3 (September 2019): 918–29. http://dx.doi.org/10.1016/j.ejor.2019.02.015.

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17

Gupta, Shanti S., and Klaus J. Miescke. "Optimum two-stage selection procedures for Weibull populations." Journal of Statistical Planning and Inference 15 (January 1986): 147–56. http://dx.doi.org/10.1016/0378-3758(86)90093-5.

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18

Stetsyuk, P., V. Stovba, and O. Khomiak. "TWO-STAGE TRANSPORTATION PROBLEM AND ITS TWO MODIFICATIONS." Journal of Numerical and Applied Mathematics, no. 1 (2022): 92–107. http://dx.doi.org/10.17721/2706-9699.2022.1.07.

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In this paper, a mathematical model of an open twostage transportation problem and its two modifications are considered. The first modification takes into account the upper bounds of transitional points capacities, the second takes into account the possibility of selection of the fixed number of transitional points, which is less than their total number. For all three cases the necessary and sufficient conditions of constraints feasibility are substantiated. The results of the computational experiments using gurobi and cplex solvers are presented.

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19

Bunea, Florentina, and Marten H. Wegkamp. "Two-stage model selection procedures in partially linear regression." Canadian Journal of Statistics 32, no. 2 (June 2004): 105–18. http://dx.doi.org/10.2307/3315936.

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20

Gaston‐Breton, Charlotte, and Oscar Martín Martín. "International market selection and segmentation: a two‐stage model." International Marketing Review 28, no. 3 (May 31, 2011): 267–90. http://dx.doi.org/10.1108/02651331111132857.

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21

Chen, Yanju, and Ye Wang. "Two-Stage Fuzzy Portfolio Selection Problem with Transaction Costs." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/675157.

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This paper studies a two-period portfolio selection problem. The problem is formulated as a two-stage fuzzy portfolio selection model with transaction costs, in which the future returns of risky security are characterized by possibility distributions. The objective of the proposed model is to achieve the maximum utility in terms of the expected value and variance of the final wealth. Given the first-stage decision vector and a realization of fuzzy return, the optimal value expression of the second-stage programming problem is derived. As a result, the proposed two-stage model is equivalent to a single-stage model, and the analytical optimal solution of the two-stage model is obtained, which helps us to discuss the properties of the optimal solution. Finally, some numerical experiments are performed to demonstrate the new modeling idea and the effectiveness. The computational results provided by the proposed model show that the more risk-averse investor will invest more wealth in the risk-free security. They also show that the optimal invested amount in risky security increases as the risk-free return decreases and the optimal utility increases as the risk-free return increases, whereas the optimal utility increases as the transaction costs decrease. In most instances the utilities provided by the proposed two-stage model are larger than those provided by the single-stage model.

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22

Meng, Jiana, Hongfei Lin, and Yuhai Yu. "A two-stage feature selection method for text categorization." Computers & Mathematics with Applications 62, no. 7 (October 2011): 2793–800. http://dx.doi.org/10.1016/j.camwa.2011.07.045.

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23

Bhandari, Subir Kumar, and Rahul Mukerjee. "Two- and multi-stage selection procedures for Weibull populations." Journal of Statistical Planning and Inference 30, no. 1 (January 1992): 73–81. http://dx.doi.org/10.1016/0378-3758(92)90108-5.

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24

Hayter, Anthony J. "On the selection probabilities of two-stage decision procedures." Journal of Statistical Planning and Inference 38, no. 2 (February 1994): 223–36. http://dx.doi.org/10.1016/0378-3758(94)90036-1.

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25

Zhao, Xuehua, Daoliang Li, Bo Yang, Huiling Chen, Xinbin Yang, Chenglong Yu, and Shuangyin Liu. "A two-stage feature selection method with its application." Computers & Electrical Engineering 47 (October 2015): 114–25. http://dx.doi.org/10.1016/j.compeleceng.2015.08.011.

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26

Koulamas, Christos, and S. S. Panwalkar. "Job selection in two-stage shops with ordered machines." Computers & Industrial Engineering 88 (October 2015): 350–53. http://dx.doi.org/10.1016/j.cie.2015.07.020.

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27

Yang, Hui, and Minjie Zhang. "Two-stage statistical language models for text database selection." Information Retrieval 9, no. 1 (January 2006): 5–31. http://dx.doi.org/10.1007/s10791-005-5719-z.

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28

Uysal, Alper Kursat. "On Two-Stage Feature Selection Methods for Text Classification." IEEE Access 6 (2018): 43233–51. http://dx.doi.org/10.1109/access.2018.2863547.

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29

Muravlev, I. O., M. A. Surkov, E. V. Tarasov, and N. F. Uvarov. "Fuse Selection for the Two-Stage Explosive Type Switches." IOP Conference Series: Materials Science and Engineering 189 (April 2017): 012004. http://dx.doi.org/10.1088/1757-899x/189/1/012004.

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30

Zhang, Shu, Tiejun Zhao, Dequan Zheng, and Hua Zhao. "Two-stage sentence selection approach for multi-document summarization." Journal of Electronics (China) 25, no. 4 (July 2008): 562–67. http://dx.doi.org/10.1007/s11767-007-0181-2.

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31

Cao, Qing, and Qiong Wang. "Optimizing vendor selection in a two-stage outsourcing process." Computers & Operations Research 34, no. 12 (December 2007): 3757–68. http://dx.doi.org/10.1016/j.cor.2006.01.013.

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32

Cook, L. M. "A two–stage model for Cepaea polymorphism." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 353, no. 1375 (October 29, 1998): 1577–93. http://dx.doi.org/10.1098/rstb.1998.0311.

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The history of the study of snails in the genus Cepaea is briefly outlined. Cepaea nemoralis and C. hortensis are polymorphic for genetically controlled shell colour and banding, which has been the main interest of the work covered. Random drift, selective predation and climatic selection, both at a macro– and micro–scale, all affect gene frequency. The usual approach to understanding maintenance of the polymorphism, has been to look for centripetal effects on frequency. Possible processes include balance of mutation pressure and drift, heterozygote advantage, relational balance heterosis, frequency–dependent predation, multi–niche selective balance, or some combination of these. Mutational balance is overlaid by more substantial forces. There is some evidence for heterosis. Predation by birds may protect the polymorphism, and act apostatically to favour distinct morphs. Although not substantiated for Cepaea , many studies show that predators behave in the appropriate manner, while shell colour polymorphisms in molluscs occur most commonly in species exposed to visually searching predators. It is not known whether different thermal properties of the shells help to generate equilibria. Migration between colonies is probably greater than originally thought. The present geographical range has been occupied for less than 5000 generations. Climatic and human modification alter snail habitats relatively rapidly, which in turn changes selection pressures. A simple simulation shows that migration coupled with selection which fluctuates but is not centripetal, may retain polymorphism for sufficiently long to account for the patterns we see today. There may therefore be a two–stage basis to the polymorphism, comprising long–term but weak balancing forces coupled with fluctuating selection which does not necessarily balance but results in very slow elimination. Persistence of genetic variants in this way may provide the conditions for evolution of a balanced genome.

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33

Jēkabsons, Gints. "Evaluation of Fingerprint Selection Algorithms for Two-Stage Plagiarism Detection." Applied Computer Systems 26, no. 2 (December 1, 2021): 178–82. http://dx.doi.org/10.2478/acss-2021-0022.

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Abstract Generally, the process of plagiarism detection can be divided into two main stages: source retrieval and text alignment. The paper evaluates and compares effectiveness of five fingerprint selection algorithms used during the source retrieval stage: Every p-th, 0 mod p, Winnowing, Frequency-biased Winnowing (FBW) and Modified FBW (MFBW). The algorithms are evaluated on a dataset containing plagiarism cases in Bachelor and Master Theses written in English in the field of computer science. The best performance is reached by 0 mod p, Winnowing and MFBW. For these algorithms, reduction of fingerprint size from 100 % to about 20 % kept the effectiveness at approximately the same level. Moreover, MFBW sends overall fewer document pairs to the text alignment stage, thus also reducing the computational cost of the process. The software developed for this study is freely available at the author’s website http://www.cs.rtu.lv/jekabsons/.

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34

Lee, Lung-fei. "Semiparametric two-stage estimation of sample selection models subject to Tobit-type selection rules." Journal of Econometrics 61, no. 2 (April 1994): 305–44. http://dx.doi.org/10.1016/0304-4076(94)90088-4.

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35

Beattie, Scott D., Duncan K. H. Fong, and Dennis K. J. Lin. "A Two-Stage Bayesian Model Selection Strategy for Supersaturated Designs." Technometrics 44, no. 1 (February 2002): 55–63. http://dx.doi.org/10.1198/004017002753398326.

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36

Chuang, Li-Yeh, Chao-Hsuan Ke, Hsueh-Wei Chang, and Cheng-Hong Yang. "A Two-Stage Feature Selection Method for Gene Expression Data." OMICS: A Journal of Integrative Biology 13, no. 2 (April 2009): 127–37. http://dx.doi.org/10.1089/omi.2008.0083.

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37

Kumar, Narinder, Gobind P. Mehta, and Virender Kumar*. "A Class of Two-Stage Selection Procedures Using L-Statistics." Allgemeines Statistisches Archiv 89, no. 3 (August 2005): 241–61. http://dx.doi.org/10.1007/s10182-005-0203-z.

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38

Lu, Lin, Xiao-He Shi, Su-Jun Li, Zhi-Qun Xie, Yong-Li Feng, Wen-Cong Lu, Yi-Xue Li, Haipeng Li, and Yu-Dong Cai. "Protein sumoylation sites prediction based on two-stage feature selection." Molecular Diversity 14, no. 1 (May 27, 2009): 81–86. http://dx.doi.org/10.1007/s11030-009-9149-5.

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39

THALL, PETER F., RICHARD SIMON, and SUSAN S. ELLENBERG. "Two-stage selection and testing designs for comparative clinical trials." Biometrika 75, no. 2 (1988): 303–10. http://dx.doi.org/10.1093/biomet/75.2.303.

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40

Huang, XG, YS Wong, and JG Wang. "A two-stage manufacturing partner selection framework for virtual enterprises." International Journal of Computer Integrated Manufacturing 17, no. 4 (June 2004): 294–304. http://dx.doi.org/10.1080/09511920310001654292.

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41

Bebu, Ionut, George Luta, and Vladimir Dragalin. "Likelihood inference for a two-stage design with treatment selection." Biometrical Journal 52, no. 6 (September 3, 2010): 811–22. http://dx.doi.org/10.1002/bimj.200900170.

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42

Settembrini, Fernanda, Stefano Bonomi, and Chiara Gregorelli. "Patient Selection and Aesthetic Outcomes in One-Stage versus Two-Stage Implant Breast Reconstruction." Plastic and Reconstructive Surgery 129, no. 2 (February 2012): 364e—366e. http://dx.doi.org/10.1097/prs.0b013e318230be38.

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43

QUINTERO-BERMUDEZ, MANUEL ANDRES, MATTY JANSSEN, JÉRÉMIE COHEN, and PAUL STUART. "Early design-stage biorefinery process selection." November 2012 11, no. 11 (December 1, 2012): 9–16. http://dx.doi.org/10.32964/tj11.11.9.

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A methodology for early design-stage evaluation of biorefinery processes that uses multicriteria decision-making (MCDM) panels was developed with the goal of screening out less promising options. Panel members were asked to rate the relative importance of a set of evaluation criteria for biorefinery process implementation at a pulp and paper mill. Three different panels were conducted, each with this same objective. This report compares and assesses the results from the panels. Two of the MCDM panels were comprised of biorefinery specialists of similar backgrounds from government and academia, and the third panel consisted of pulp and paper industry decision makers. In general, consensus was high between all three panels, and especially between the two biorefinery specialist panels; however, certain differences were noted with the industry panel. For three out of eight criteria, these differences were attributable to a difference in the perspective of the forest industry decision makers and biorefinery specialists. For example, for the return on investment criteria, the specialist panels gave a significantly higher importance to current information from technology suppliers because it provides an important short-term perspective. In contrast, the forest industry decision makers thought that information about a particular technology should be critiqued and adjusted based on a systematic assessment of likely future process performance. Differences between the other criteria were attributed to differing interpretations of the criteria.

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44

Song, Shuangbao, Xingqian Chen, Zheng Tang, and Yuki Todo. "A Two-Stage Method Based on Multiobjective Differential Evolution for Gene Selection." Computational Intelligence and Neuroscience 2021 (December 20, 2021): 1–16. http://dx.doi.org/10.1155/2021/5227377.

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Microarray gene expression data provide a prospective way to diagnose disease and classify cancer. However, in bioinformatics, the gene selection problem, i.e., how to select the most informative genes from thousands of genes, remains challenging. This problem is a specific feature selection problem with high-dimensional features and small sample sizes. In this paper, a two-stage method combining a filter feature selection method and a wrapper feature selection method is proposed to solve the gene selection problem. In contrast to common methods, the proposed method models the gene selection problem as a multiobjective optimization problem. Both stages employ the same multiobjective differential evolution (MODE) as the search strategy but incorporate different objective functions. The three objective functions of the filter method are mainly based on mutual information. The two objective functions of the wrapper method are the number of selected features and the classification error of a naive Bayes (NB) classifier. Finally, the performance of the proposed method is tested and analyzed on six benchmark gene expression datasets. The experimental results verified that this paper provides a novel and effective way to solve the gene selection problem by applying a multiobjective optimization algorithm.

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45

Ma, Xin. "Supplier Selection Model Based on Two Stage Signal Game and Reputation Mechanism." Advanced Materials Research 214 (February 2011): 50–54. http://dx.doi.org/10.4028/www.scientific.net/amr.214.50.

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Asymmetric information will lead to adverse selection in the supplier selection in the supply. One the basis of game theory and information economics theories, a two stage supplier selection signal game reputation model is presented. The optimal price signal and utility of supplier are derived under symmetric and asymmetric information. The optimal signal of supplier will be strengthen and the utility at the second stage will also be increased if the supplier sets up reputation at the first stage, and it also shown that more utilities can be achieved when a supplier uses reputation.

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46

Jeong, Nam-Hoon, Jae-Ho Choi, Geon Lee, Ji-Hoon Park, and Kyung-Tae Kim. "Feature Selection for SAR Target Discrimination and Efficient Two-Stage Detection Method." Remote Sensing 14, no. 16 (August 19, 2022): 4044. http://dx.doi.org/10.3390/rs14164044.

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Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their performance depends on the characteristics of the images, and both efficiency and performance deteriorate when the features are used indiscriminately. In this study, we propose a two-stage detection framework to ensure efficient and superior detection performance in TSX images, using previously studied features. The proposed method consists of two stages. The first stage uses simple features to eliminate misdetections. Next, the discrimination performance for the target and clutter of each feature is evaluated and those features suitable for the image are selected. In addition, the Karhunen–Loève (KL) transform reduces the redundancy of the selected features and maximizes discrimination performance. By applying the proposed method to actual TerraSAR-X (TSX) images, the majority of the identified clusters of false detections were excluded, and the target of interest could be distinguished.

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47

P, Ashokkumar, Siva Shankar G, Gautam Srivastava, Praveen Kumar Reddy Maddikunta, and Thippa Reddy Gadekallu. "A Two-stage Text Feature Selection Algorithm for Improving Text Classification." ACM Transactions on Asian and Low-Resource Language Information Processing 20, no. 3 (May 2021): 1–19. http://dx.doi.org/10.1145/3425781.

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As the number of digital text documents increases on a daily basis, the classification of text is becoming a challenging task. Each text document consists of a large number of words (or features) that drive down the efficiency of a classification algorithm. This article presents an optimized feature selection algorithm designed to reduce a large number of features to improve the accuracy of the text classification algorithm. The proposed algorithm uses noun-based filtering, a word ranking that enhances the performance of the text classification algorithm. Experiments are carried out on three benchmark datasets, and the results show that the proposed classification algorithm has achieved the maximum accuracy when compared to the existing algorithms. The proposed algorithm is compared to Term Frequency-Inverse Document Frequency, Balanced Accuracy Measure, GINI Index, Information Gain, and Chi-Square. The experimental results clearly show the strength of the proposed algorithm.

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48

Jie, ZHANG, SHENG Xia, ZHANG Peng, QIN Wei, and ZHAO Xinming. "Two-stage Unsupervised Feature Selection Method Oriented to Manufacturing Procedural Data." Journal of Mechanical Engineering 55, no. 17 (2019): 133. http://dx.doi.org/10.3901/jme.2019.17.133.

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49

Qiu, Chenye. "A hybrid two-stage feature selection method based on differential evolution." Journal of Intelligent & Fuzzy Systems 39, no. 1 (July 17, 2020): 871–84. http://dx.doi.org/10.3233/jifs-191765.

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50

Wang, Chia-Nan, Thi-Ly Nguyen, and Thanh-Tuan Dang. "Two-Stage Fuzzy MCDM for Green Supplier Selection in Steel Industry." Intelligent Automation & Soft Computing 33, no. 2 (2022): 1245–60. http://dx.doi.org/10.32604/iasc.2022.024548.

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