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Journal articles on the topic 'Selection Combining'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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1

Zaremba, Adam. "Combining Equity Country Selection Strategies." Contemporary Economics 11, no. 1 (March 31, 2017): 107–26. http://dx.doi.org/10.5709/ce.1897-9254.231.

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2

Alouini, Mohamed-slim, and Hong-chuan Yang. "Minimum estimation and combining generalized selection combining (MEC-GSC)." IEEE Transactions on Wireless Communications 6, no. 2 (February 2007): 526–32. http://dx.doi.org/10.1109/twc.2007.05225.

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3

Ali, Shaik Mohammed, and Hemanta Kumar Sahu. "SSK modulation performance with relay selection and selection combining." Internet Technology Letters 2, no. 6 (February 20, 2019): e92. http://dx.doi.org/10.1002/itl2.92.

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4

Dobbie, Lorraine, John C. Maule, and Anthony J. Brookes. "Combining methods for direct cDNA selection." Technical Tips Online 3, no. 1 (January 1998): 66–68. http://dx.doi.org/10.1016/s1366-2120(08)70102-8.

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5

Adachi, F., and J. D. Parsons. "Random FM noise with selection combining." IEEE Transactions on Communications 36, no. 6 (June 1988): 752–50. http://dx.doi.org/10.1109/26.2796.

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6

Albanis, George, and Roy Batchelor. "Combining heterogeneous classifiers for stock selection." Intelligent Systems in Accounting, Finance and Management 15, no. 1-2 (January 2007): 1–21. http://dx.doi.org/10.1002/isaf.282.

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7

Proschan, Michael A., and Sally A. Hunsberger. "Combining Treatment Selection and Definitive Testing." Biometrical Journal 48, no. 4 (August 2006): 690–92. http://dx.doi.org/10.1002/bimj.200610247.

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8

Chrysostomou, Kyriacos, Sherry Y. Chen, and Xiaohui Liu. "Combining multiple classifiers for wrapper feature selection." International Journal of Data Mining, Modelling and Management 1, no. 1 (2008): 91. http://dx.doi.org/10.1504/ijdmmm.2008.022539.

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9

Lioumpas, Athanasios, George Karagiannidis, and Theodoros Tsiftsis. "Adaptive generalized selection combining (A-GSC) receivers." IEEE Transactions on Wireless Communications 7, no. 12 (December 2008): 5214–19. http://dx.doi.org/10.1109/t-wc.2008.071087.

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10

Verheijen, J. H. M., F. P. A. Coolent, and P. van der Laan. "Combining two classical approaches for statistical selection." Communications in Statistics - Theory and Methods 26, no. 6 (January 1997): 1291–312. http://dx.doi.org/10.1080/03610929708831983.

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11

Nagarajan, V., and M. Balasaraswathi. "Threshold-based selection combining for relay networks." Computers & Electrical Engineering 60 (May 2017): 129–39. http://dx.doi.org/10.1016/j.compeleceng.2017.02.007.

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12

Assumpção Silva, Ronan, Alceu S. Britto, Fabricio Enembreck, Robert Sabourin, and Luiz S. Oliveira. "Selecting and Combining Classifiers Based on Centrality Measures." International Journal on Artificial Intelligence Tools 29, no. 03n04 (June 2020): 2060004. http://dx.doi.org/10.1142/s0218213020600040.

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Centrality measures have been helping to explain the behavior of objects, given their relation, in a wide variety of problems, since sociology to chemistry. This work considers these measures to assess the importance of every classifier belonging to an ensemble of classifiers, aiming to improve a Multiple Classifier System (MCS). Assessing the classifier’s importance by employing centrality measures, inspired two different approaches: one for selecting classifiers and another for fusion. The selection approach, called Centrality Based Selection (CBS), adopts a trade-off between the classifier’s accuracy and their diversity. The sub-optimal selected subset presents good results against selection methods from the literature, being superior in 67.22% of the cases. The second approach, the integration, is named Centrality Based Fusion (CBF). This approach is a weighted combination method, which is superior to literature in 70% of the cases.
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13

R, Swaminathan, Rajarshi Roy, and M. D. Selvaraj. "Performance Comparison of Selection Combining With Full CSI and Switch-and-Examine Combining With and Without Post-Selection." IEEE Transactions on Vehicular Technology 65, no. 5 (May 2016): 3217–30. http://dx.doi.org/10.1109/tvt.2015.2434836.

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14

Paik, Minhui, and Yuhong Yang. "Combining Nearest Neighbor Classifiers Versus Cross-Validation Selection." Statistical Applications in Genetics and Molecular Biology 3, no. 1 (January 9, 2004): 1–19. http://dx.doi.org/10.2202/1544-6115.1054.

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Various discriminant methods have been applied for classification of tumors based on gene expression profiles, among which the nearest neighbor (NN) method has been reported to perform relatively well. Usually cross-validation (CV) is used to select the neighbor size as well as the number of variables for the NN method. However, CV can perform poorly when there is considerable uncertainty in choosing the best candidate classifier. As an alternative to selecting a single “winner," we propose a weighting method to combine the multiple NN rules. Four gene expression data sets are used to compare its performance with CV methods. The results show that when the CV selection is unstable, the combined classifier performs much better.
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15

Koshiyama, Adriano S., Tatiana Escovedo, Douglas M. Dias, Marley M. B. R. Vellasco, and Marco A. C. Pacheco. "Combining Forecasts." International Journal of Natural Computing Research 3, no. 3 (July 2012): 41–58. http://dx.doi.org/10.4018/jncr.2012070103.

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Combining forecasts is a common practice in time series analysis. This technique involves weighing each estimate of different models in order to minimize the error between the resulting output and the target. This work presents a novel methodology, aiming to combine forecasts using genetic programming, a metaheuristic that searches for a nonlinear combination and selection of forecasters simultaneously. To present the method, the authors made three different tests comparing with the linear forecasting combination, evaluating both in terms of RMSE and MAPE. The statistical analysis shows that the genetic programming combination outperforms the linear combination in two of the three tests evaluated.
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16

Yuan, Liming, Jiafeng Liu, and Xianglong Tang. "Combining example selection with instance selection to speed up multiple-instance learning." Neurocomputing 129 (April 2014): 504–15. http://dx.doi.org/10.1016/j.neucom.2013.09.008.

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17

LEE, HYUN AH, JUNTAE YOON, and GIL CHANG KIM. "Translation Selection by Combining Multiple Measures for Sense Disambiguation and Word Selection." International Journal of Computer Processing of Languages 16, no. 03 (September 2003): 219–39. http://dx.doi.org/10.1142/s0219427903000905.

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18

Gonzalez-Granadillo, Gustavo, Christophe Ponchel, Gregory Blanc, and Hervé Debar. "Combining Technical and Financial Impacts for Countermeasure Selection." Electronic Proceedings in Theoretical Computer Science 165 (October 13, 2014): 1–14. http://dx.doi.org/10.4204/eptcs.165.1.

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19

Badarneh, Osamah S., Daniel Benevides da Costa, Mustapha Benjillali, and Mohamed-Slim Alouini. "Selection Combining Over Double $\alpha$-$\mu$ Fading Channels." IEEE Transactions on Vehicular Technology 69, no. 3 (March 2020): 3444–48. http://dx.doi.org/10.1109/tvt.2020.2969224.

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20

Zhang, Yi, Chris Ding, and Tao Li. "Gene selection algorithm by combining reliefF and mRMR." BMC Genomics 9, Suppl 2 (2008): S27. http://dx.doi.org/10.1186/1471-2164-9-s2-s27.

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21

Polak, Adam G. "Indirect measurements: combining parameter selection with ridge regression." Measurement Science and Technology 12, no. 3 (February 16, 2001): 278–87. http://dx.doi.org/10.1088/0957-0233/12/3/306.

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22

Deng, Yansha, Maged Elkashlan, Phee Lep Yeoh, Nan Yang, and Ranjan K. Mallik. "Cognitive MIMO Relay Networks With Generalized Selection Combining." IEEE Transactions on Wireless Communications 13, no. 9 (September 2014): 4911–22. http://dx.doi.org/10.1109/twc.2014.2326672.

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23

Tsai, Chih-Fong, and Fu-Yu Chang. "Combining instance selection for better missing value imputation." Journal of Systems and Software 122 (December 2016): 63–71. http://dx.doi.org/10.1016/j.jss.2016.08.093.

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24

Selvaraj, M. D., and Ranjan K. Mallik. "Single-Relay Cooperative Diversity with Scaled Selection Combining." IEEE Transactions on Communications 59, no. 3 (March 2011): 701–7. http://dx.doi.org/10.1109/tcomm.2011.010411.100115.

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25

Yao Ma, Xiaodai Dong, and Hong-Chuan Yang. "Asymptotic Performance of Threshold-Based Generalized Selection Combining." IEEE Transactions on Vehicular Technology 58, no. 5 (2009): 2579–85. http://dx.doi.org/10.1109/tvt.2008.2010050.

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26

Grove, T. Z., M. Hands, and L. Regan. "Creating novel proteins by combining design and selection." Protein Engineering Design and Selection 23, no. 6 (March 19, 2010): 449–55. http://dx.doi.org/10.1093/protein/gzq015.

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27

Campillo, F., J. van Santen, and E. R. Banga. "Combining phrasing and unit selection in intonation modelling." Electronics Letters 44, no. 7 (2008): 501. http://dx.doi.org/10.1049/el:20083276.

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28

Hsu, Hui-Huang, Cheng-Wei Hsieh, and Ming-Da Lu. "Hybrid feature selection by combining filters and wrappers." Expert Systems with Applications 38, no. 7 (July 2011): 8144–50. http://dx.doi.org/10.1016/j.eswa.2010.12.156.

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29

Karaevli, İzzet Levent, Güneş Karabulut Kurt, and İbrahim Altunbaş. "Analysis of cooperative MIMO transmission system with transmit antenna selection and selection combining." Wireless Communications and Mobile Computing 12, no. 14 (November 10, 2010): 1266–75. http://dx.doi.org/10.1002/wcm.1054.

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30

Marcón, Florencia, Eric J. Martínez, Alex L. Zilli, Gustavo R. Rodríguez, Elsa A. Brugnoli, and Carlos A. Acuña. "Recurrent phenotypic selection and recurrent selection based on combining ability in tetraploid bahiagrass." Crop Science 60, no. 3 (March 31, 2020): 1386–97. http://dx.doi.org/10.1002/csc2.20137.

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31

El-Mahdy, Ahmed El-Sayed, and Alia Wassef. "Multiple Antenna Selection of Multi-Antenna Relays Using Threshold-Generalized Selection Combining Scheme." Journal of Computational Intelligence and Electronic Systems 3, no. 4 (December 1, 2014): 239–43. http://dx.doi.org/10.1166/jcies.2014.1107.

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32

LI, YUN, BAO-LIANG LU, and TENG-FEI ZHANG. "COMBINING FEATURE SELECTION WITH EXTRACTION: UNSUPERVISED FEATURE SELECTION BASED ON PRINCIPAL COMPONENT ANALYSIS." International Journal on Artificial Intelligence Tools 18, no. 06 (December 2009): 883–904. http://dx.doi.org/10.1142/s0218213009000445.

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Principal components analysis (PCA) is a popular linear feature extractor, and widely used in signal processing, face recognition, etc. However, axes of the lower-dimensional space, i.e., principal components, are a set of new variables carrying no clear physical meanings. Thus we propose unsupervised feature selection algorithms based on eigenvectors analysis to identify critical original features for principal component. The presented algorithms are based on k-nearest neighbor rule to find the predominant row components and eight new measures are proposed to compute the correlation between row components in transformation matrix. Experiments are conducted on benchmark data sets and facial image data sets for gender classification to show their superiorities.
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33

Peng, Cheng, Dianzhuang Feng, and Sidai Guo. "Material Selection in Green Design: A Method Combining DEA and TOPSIS." Sustainability 13, no. 10 (May 14, 2021): 5497. http://dx.doi.org/10.3390/su13105497.

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In order to rationalize material selection in green design, this study presents an attempt to combine the methods of generalized Data Envelopment Analysis (DEA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). By establishing a green material index system, the G-CCR model of generalized DEA was first used to select effective materials from the candidate samples, and TOPSIS was then used to sort the effective suppliers. The combined DEA/TOPSIS model helps to rank the materials by quality, and then integrate both the merits ofG-CCR model and the key characteristics of TOPSIS. The results of this study showed that the combined DEA/TOPSIS model can screen and exclude materials with poor performance when selecting wood for the furniture industry. Therefore, the combined model that is presented in this study provides a more rational and evidentiary basis for material selection in green design.
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34

Junior de Pelegrin, Alan, Maicon Nardino, Ivan Ricardo Carvalho, Vinicius Jardel Szareski, Mauricio Ferrari, Giordano Gelain Conte, Antonio Costa de Oliveira, Velci Queiroz de Souza, and Luciano Carlos da Maia. "COMBINING ABILITY AS A CRITERION FOR WHEAT PARENTS SELECTION." Functional Plant Breeding Journal 2, no. 1 (June 9, 2020): 35–45. http://dx.doi.org/10.35418/2526-4117/v2n1a4.

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35

Rui, Xian-yi. "Average SNR of maximum ratio transmission with selection combining." Journal of Zhejiang University-SCIENCE A 10, no. 12 (December 2009): 1683–87. http://dx.doi.org/10.1631/jzus.a0920451.

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36

Nakano, Tomofumi, and Nobuhiro Inuzuka. "Solving selection problems combining relational learning and Bayesian learning." International Journal of Knowledge-based and Intelligent Engineering Systems 11, no. 1 (February 1, 2007): 35–45. http://dx.doi.org/10.3233/kes-2007-11103.

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37

NARIEDA, Shusuke, Hiromichi OGASAWARA, and Hiroshi NARUSE. "Spectrum Sensing with Selection Diversity Combining in Cognitive Radio." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E103.A, no. 8 (August 1, 2020): 978–86. http://dx.doi.org/10.1587/transfun.2019eap1167.

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38

Vandiver, Jeremy W., and K. Diane Beavers. "Combining oral anticoagulation and antiplatelet therapies: appropriate patient selection." Journal of Thrombosis and Thrombolysis 45, no. 3 (February 24, 2018): 423–31. http://dx.doi.org/10.1007/s11239-018-1635-0.

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39

Korpela, Jukka, Antti Lehmusvaara, and Jukka Nisonen. "Warehouse operator selection by combining AHP and DEA methodologies." International Journal of Production Economics 108, no. 1-2 (July 2007): 135–42. http://dx.doi.org/10.1016/j.ijpe.2006.12.046.

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40

WU, Lian-hai, Jia-ru LIN, Kai NIU, and Zhi-qiang HE. "Error analysis of generalized selection combining over relay channel." Journal of China Universities of Posts and Telecommunications 17 (July 2010): 50–66. http://dx.doi.org/10.1016/s1005-8885(09)60608-7.

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41

Kumar, Vaibhav, Barry Cardiff, and Mark F. Flanagan. "Performance analysis of NOMA with generalised selection combining receivers." Electronics Letters 55, no. 25 (December 2019): 1364–67. http://dx.doi.org/10.1049/el.2019.2948.

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42

Ning Kong and L. B. Milstein. "Average SNR of a generalized diversity selection combining scheme." IEEE Communications Letters 3, no. 3 (March 1999): 57–59. http://dx.doi.org/10.1109/4234.752901.

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43

Ritcey, J. A., and M. Azizoglu. "Performance analysis of generalized selection combining with switching constraints." IEEE Communications Letters 4, no. 5 (May 2000): 152–54. http://dx.doi.org/10.1109/4234.846495.

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44

Friswell, M. I., J. E. Mottershead, and H. Ahmadian. "Combining Subset Selection and Parameter Constraints in Model Updating." Journal of Vibration and Acoustics 120, no. 4 (October 1, 1998): 854–59. http://dx.doi.org/10.1115/1.2893911.

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Model updating often produces sets of equations whose solution are ill-conditioned and extra information must be used to produce a well-conditioned estimation problem. One possibility is to change all the parameters, but to introduce extra constraints, for example by taking the minimum norm solution. This paper takes a different approach, by considering only a subset of the parameters to be in error. The critical decision is then the choice of parameters to include in the subset. The methods of subset selection are outlined and extended to the selection of groups of parameters. The incorporation of side constraints is considered and demonstrated using an experimental example.
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45

Hormaza, J. I. "Early selection in cherry combining RAPDs with embryo culture." Scientia Horticulturae 79, no. 1-2 (February 1999): 121–26. http://dx.doi.org/10.1016/s0304-4238(98)00204-0.

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46

Bao, V. N. Q., and H. Y. Kong. "Distributed switch and stay combining for selection relay networks." IEEE Communications Letters 13, no. 12 (December 2009): 914–16. http://dx.doi.org/10.1109/lcomm.2009.12.091440.

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47

Sopadang, Apichat, and Ruth Banomyong. "Combining AHP and TOPSIS method for logistics hub selection." International Journal of Management and Decision Making 15, no. 2 (2016): 134. http://dx.doi.org/10.1504/ijmdm.2016.077764.

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48

Yao Ma and Jinghua Jin. "Unified Performance Analysis of Hybrid-Selection/Equal-Gain Combining." IEEE Transactions on Vehicular Technology 56, no. 4 (July 2007): 1866–73. http://dx.doi.org/10.1109/tvt.2007.897650.

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49

Sagias, Nikos C., Panagiotis Varzakas, George S. Tombras, and George K. Karagiannidis. "Average channel capacity for generalized-selection combining RAKE receivers." European Transactions on Telecommunications 15, no. 5 (2004): 497–500. http://dx.doi.org/10.1002/ett.1002.

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50

Alouini, Mohamed-Slim, and Marvin K. Simon. "Performance of generalized selection combining over Weibull fading channels." Wireless Communications and Mobile Computing 6, no. 8 (2006): 1077–84. http://dx.doi.org/10.1002/wcm.294.

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