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Journal articles on the topic 'Ordinal information'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Yager, Ronald R. "Aggregation of ordinal information." Fuzzy Optimization and Decision Making 6, no. 3 (September 13, 2007): 199–219. http://dx.doi.org/10.1007/s10700-007-9008-8.

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2

BORDOGNA, GLORIA, and GABRIELLA PASI. "AN ORDINAL INFORMATION RETRIEVAL MODEL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, supp01 (September 2001): 63–75. http://dx.doi.org/10.1142/s0218488501000995.

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In this paper an ordinal Information Retrieval model is proposed, which is formalised within fuzzy set theory and is based on the notion of linguistic granules of information. Linguistic expressions are defined to represent and manage the importance of both the index terms as descriptors of the information items and the query terms (content selectors) as descriptors of users' needs. The advantage of this approach with respect to the (numeric) fuzzy IR models is that the query evaluation mechanism and the definition of the importance semantics are simplified.
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3

Mon-Williams, Mark, and James R. Tresilian. "Ordinal depth information from accommodation?" Ergonomics 43, no. 3 (March 2000): 391–404. http://dx.doi.org/10.1080/001401300184486.

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4

de Cooman, Gert. "Confidence relations and ordinal information." Information Sciences 104, no. 3-4 (February 1998): 241–77. http://dx.doi.org/10.1016/s0020-0255(97)00066-2.

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5

Hu, QingHua, MaoZu Guo, DaRen Yu, and JinFu Liu. "Information entropy for ordinal classification." Science China Information Sciences 53, no. 6 (May 29, 2010): 1188–200. http://dx.doi.org/10.1007/s11432-010-3117-7.

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6

Alcalde-Unzu, Jorge, Ricardo Arlegi, and Miguel A. Ballester. "Uncertainty with ordinal likelihood information." Social Choice and Welfare 41, no. 2 (July 27, 2012): 397–425. http://dx.doi.org/10.1007/s00355-012-0689-8.

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7

Qi, Haoliang, Sheng Li, Jianfeng Gao, Zhongyuan Han, and Xinsong Xia. "Ordinal Regression for Information Retrieval." Journal of Electronics (China) 25, no. 1 (January 2008): 120–24. http://dx.doi.org/10.1007/s11767-006-0256-5.

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8

Amigó, J. M., T. Aschenbrenner, W. Bunk, and R. Monetti. "Information-theoretical applications of ordinal patterns." IEICE Proceeding Series 2 (March 17, 2014): 182–85. http://dx.doi.org/10.15248/proc.2.182.

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9

Punkka, Antti, and Ahti Salo. "Preference Programming with incomplete ordinal information." European Journal of Operational Research 231, no. 1 (November 2013): 141–50. http://dx.doi.org/10.1016/j.ejor.2013.05.003.

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10

Tian, Qing, and Songcan Chen. "A novel ordinal learning strategy: Ordinal nearest-centroid projection." Knowledge-Based Systems 88 (November 2015): 144–53. http://dx.doi.org/10.1016/j.knosys.2015.07.037.

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11

Pietro, Amenta, Simonetti Biagio, and Beh Eric. "Single Ordinal Correspondence Analysis with External Information." Asian Journal of Mathematics & Statistics 1, no. 1 (December 15, 2007): 34–42. http://dx.doi.org/10.3923/ajms.2008.34.42.

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12

"Single Ordinal Correspondence Analysis with External Information*." Asian Journal of Mathematics & Statistics 3, no. 4 (September 15, 2010): 287–95. http://dx.doi.org/10.3923/ajms.2010.287.295.

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13

YAGER, RONALD R. "DECISION MAKING UNDER UNCERTAINTY WITH ORDINAL INFORMATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 05 (October 1999): 483–500. http://dx.doi.org/10.1142/s021848859900043x.

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We consider the problem of decision making under uncertainty where the payoffs are measured on an ordinal scale. First the case in which no information is available with respect to the uncertainty is studied. Here decision functions are obtained which reflect the attitude of the decision maker. Next the case where a possibility distribution exists with respect to uncertain variable is considered. Finally we look at the case where both possibilistic and probabilistic uncertainty co-exist.
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14

Godo, L., and V. Torra. "On aggregation operators for ordinal qualitative information." IEEE Transactions on Fuzzy Systems 8, no. 2 (April 2000): 143–54. http://dx.doi.org/10.1109/91.842149.

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15

Wang, Zeshen, and Ferjan Ormeling. "The Representation of Quantitative and Ordinal Information." Cartographic Journal 33, no. 2 (December 1996): 87–91. http://dx.doi.org/10.1179/caj.1996.33.2.87.

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16

Hornung, Roman. "Ordinal Forests." Journal of Classification 37, no. 1 (January 22, 2019): 4–17. http://dx.doi.org/10.1007/s00357-018-9302-x.

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17

Shaki, Samuel, and Wim Gevers. "Cultural Characteristics Dissociate Magnitude and Ordinal Information Processing." Journal of Cross-Cultural Psychology 42, no. 4 (May 2011): 639–50. http://dx.doi.org/10.1177/0022022111406100.

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18

Li, Jian, Jiakun Xu, and Qiang Zhou. "Monitoring serially dependent categorical processes with ordinal information." IISE Transactions 50, no. 7 (March 9, 2018): 596–605. http://dx.doi.org/10.1080/24725854.2018.1429695.

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19

Yager, Ronald R. "Fusion of ordinal information using weighted median aggregation." International Journal of Approximate Reasoning 18, no. 1-2 (January 1998): 35–52. http://dx.doi.org/10.1016/s0888-613x(97)10003-2.

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20

Çela, Eranda, Stephan Hafner, Roland Mestel, and Ulrich Pferschy. "Mean-variance portfolio optimization based on ordinal information." Journal of Banking & Finance 122 (January 2021): 105989. http://dx.doi.org/10.1016/j.jbankfin.2020.105989.

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21

Schroeder, Philipp Alexander, Hans-Christoph Nuerk, and Christian Plewnia. "Space in numerical and ordinal information: A common construct?" Journal of Numerical Cognition 3, no. 2 (December 22, 2017): 164–81. http://dx.doi.org/10.5964/jnc.v3i2.40.

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Space is markedly involved in numerical processing, both explicitly in instrumental learning and implicitly in mental operations on numbers. Besides action decisions, action generations, and attention, the response-related effect of numerical magnitude or ordinality on space is well documented in the Spatial-Numerical Associations of Response Codes (SNARC) effect. Here, right- over left-hand responses become relatively faster with increasing magnitude positions. However, SNARC-like behavioral signatures in non-numerical tasks with ordinal information were also observed and inspired new models integrating seemingly spatial effects of ordinal and numerical metrics. To examine this issue further, we report a comparison between numerical SNARC and ordinal SNARC-like effects to investigate group-level characteristics and individual-level deductions from generalized views, i.e., convergent validity. Participants solved order-relevant (before/after classification) and order-irrelevant tasks (font color classification) with numerical stimuli 1-5, comprising both magnitude and order information, and with weekday stimuli, comprising only ordinal information. A small correlation between magnitude- and order-related SNARCs was observed, but effects are not pronounced in order-irrelevant color judgments. On the group level, order-relevant spatial-numerical associations were best accounted for by a linear magnitude predictor, whereas the SNARC effect for weekdays was categorical. Limited by the representativeness of these tasks and analyses, results are inconsistent with a single amodal cognitive mechanism that activates space in mental processing of cardinal and ordinal information alike. A possible resolution to maintain a generalized view is proposed by discriminating different spatial activations, possibly mediated by visuospatial and verbal working memory, and by relating results to findings from embodied numerical cognition.
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22

Kim, Minyoung. "Conditional ordinal random fields for structured ordinal-valued label prediction." Data Mining and Knowledge Discovery 28, no. 2 (March 12, 2013): 378–401. http://dx.doi.org/10.1007/s10618-013-0305-2.

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23

Tague, J. "Informativeness as an ordinal utility function for information retrieval." ACM SIGIR Forum 21, no. 3-4 (March 1987): 10–17. http://dx.doi.org/10.1145/30075.30077.

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24

Kemp, Simon, and Randolph C. Grace. "When can information from ordinal scale variables be integrated?" Psychological Methods 15, no. 4 (2010): 398–412. http://dx.doi.org/10.1037/a0021462.

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25

Li, Changsheng, Qingshan Liu, Weishan Dong, Xiaobin Zhu, Jing Liu, and Hanqing Lu. "Human Age Estimation Based on Locality and Ordinal Information." IEEE Transactions on Cybernetics 45, no. 11 (November 2015): 2522–34. http://dx.doi.org/10.1109/tcyb.2014.2376517.

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26

Anshelevich, Elliot, and Wennan Zhu. "Tradeoffs Between Information and Ordinal Approximation for Bipartite Matching." Theory of Computing Systems 63, no. 7 (August 16, 2018): 1499–530. http://dx.doi.org/10.1007/s00224-018-9886-x.

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27

Singer, Gonen, Roee Anuar, and Irad Ben-Gal. "A weighted information-gain measure for ordinal classification trees." Expert Systems with Applications 152 (August 2020): 113375. http://dx.doi.org/10.1016/j.eswa.2020.113375.

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28

Chu, Wei, and S. Sathiya Keerthi. "Support Vector Ordinal Regression." Neural Computation 19, no. 3 (March 2007): 792–815. http://dx.doi.org/10.1162/neco.2007.19.3.792.

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In this letter, we propose two new support vector approaches for ordinal regression, which optimize multiple thresholds to define parallel discriminant hyperplanes for the ordinal scales. Both approaches guarantee that the thresholds are properly ordered at the optimal solution. The size of these optimization problems is linear in the number of training samples. The sequential minimal optimization algorithm is adapted for the resulting optimization problems; it is extremely easy to implement and scales efficiently as a quadratic function of the number of examples. The results of numerical experiments on some benchmark and real-world data sets, including applications of ordinal regression to information retrieval, verify the usefulness of these approaches.
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29

Zhu, Tuanfei, Yaping Lin, Yonghe Liu, Wei Zhang, and Jianming Zhang. "Minority oversampling for imbalanced ordinal regression." Knowledge-Based Systems 166 (February 2019): 140–55. http://dx.doi.org/10.1016/j.knosys.2018.12.021.

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30

Du Plessis, Phillip J., and Michael J. Greenacre. "Modelling Information Search Behaviour of Car Purchasers." South African Journal of Psychology 19, no. 3 (September 1989): 138–43. http://dx.doi.org/10.1177/008124638901900304.

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The objective in the study was to establish whether there was any relationship between certain information usage categories and four selected predictor variables namely (1) new or used car purchase, (2) other-than-white or white buyer, (3) male or female, and (4) first-time buyer or experienced buyer. Certain external sources of information (non-market dominated and market dominated) which are available to the buyer of a car and the development of a model of the probability of buyers using the source are investigated. The technique of ordinal logistic regression is assumed to be the appropriate modelling tool in this study where the response variables of interest are ordinal.
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31

Muchnik, An A. "Determinization of ordinal automata." Problems of Information Transmission 49, no. 2 (April 2013): 149–62. http://dx.doi.org/10.1134/s003294601302004x.

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32

Lei, Yiming, Haiping Zhu, Junping Zhang, and Hongming Shan. "Meta Ordinal Regression Forest for Medical Image Classification With Ordinal Labels." IEEE/CAA Journal of Automatica Sinica 9, no. 7 (July 2022): 1233–47. http://dx.doi.org/10.1109/jas.2022.105668.

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33

KELLER, KARSTEN, MATHIEU SINN, and JAN EMONDS. "TIME SERIES FROM THE ORDINAL VIEWPOINT." Stochastics and Dynamics 07, no. 02 (June 2007): 247–72. http://dx.doi.org/10.1142/s0219493707002025.

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Ordinal time series analysis is a new approach to the investigation of long and complex time series, which bases on ordinal patterns describing the order relations between the values of a time series. In this paper we consider ordinal time series analysis from the conceptional viewpoint. In particular, we introduce ordinal processes as models for ordinal time series analysis and discuss the structure of ordinal pattern distributions obtained from them. Special emphasis is on the relation of ordinal time series analysis to symbolic dynamics and to a transformation extracting the whole ordinal information contained in a time series. Finally, we consider invariance properties of ordinal time series analysis.
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34

Xu, Xinzheng, Qiaoyu Guo, Zhongnian Li, and Dechun Li. "Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis." Healthcare 10, no. 11 (November 17, 2022): 2300. http://dx.doi.org/10.3390/healthcare10112300.

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Ordinal multi-instance learning (OMIL) deals with the weak supervision scenario wherein instances in each training bag are not only multi-class but also have rank order relationships between classes, such as breast cancer, which has become one of the most frequent diseases in women. Most of the existing work has generally been to classify the region of interest (mass or microcalcification) on the mammogram as either benign or malignant, while ignoring the normal mammogram classification. Early screening for breast disease is particularly important for further diagnosis. Since early benign lesion areas on a mammogram are very similar to normal tissue, three classifications of mammograms for the improved screening of early benign lesions are necessary. In OMIL, an expert will only label the set of instances (bag), instead of labeling every instance. When labeling efforts are focused on the class of bags, ordinal classes of the instance inside the bag are not labeled. However, recent work on ordinal multi-instance has used the traditional support vector machine to solve the multi-classification problem without utilizing the ordinal information regarding the instances in the bag. In this paper, we propose a method that explicitly models the ordinal class information for bags and instances in bags. Specifically, we specify a key instance from the bag as a positive instance of bags, and design ordinal minimum uncertainty loss to iteratively optimize the selected key instances from the bags. The extensive experimental results clearly prove the effectiveness of the proposed ordinal instance-learning approach, which achieves 52.021% accuracy, 61.471% sensitivity, 47.206% specificity, 57.895% precision, and an 59.629% F1 score on a DDSM dataset.
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35

Wang, Zhenhua, Bin Fan, Gang Wang, and Fuchao Wu. "Exploring Local and Overall Ordinal Information for Robust Feature Description." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 11 (November 1, 2016): 2198–211. http://dx.doi.org/10.1109/tpami.2015.2513396.

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36

Fu, Tsu-Tan, Lung-An Li, Yih-Ming Lin, and Kamhon Kan. "A limited information estimator for the multivariate ordinal probit model." Applied Economics 32, no. 14 (November 2000): 1841–51. http://dx.doi.org/10.1080/000368400425062.

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37

MacKenzie, K. J., and L. M. Wilcox. "Second-order motion alone does not convey ordinal depth information." Journal of Vision 5, no. 8 (March 16, 2010): 145. http://dx.doi.org/10.1167/5.8.145.

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38

Ho, Y. C., C. G. Cassandras, C. H. Chen, and L. Dai. "Ordinal Optimisation and Simulation." Journal of the Operational Research Society 51, no. 4 (April 2000): 490. http://dx.doi.org/10.2307/254177.

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39

Liu, Yang, Yan Liu, and Keith Chan. "Ordinal Regression via Manifold Learning." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 398–403. http://dx.doi.org/10.1609/aaai.v25i1.7937.

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Ordinal regression is an important research topic in machine learning. It aims to automatically determine the implied rating of a data item on a fixed, discrete rating scale. In this paper, we present a novel ordinal regression approach via manifold learning, which is capable of uncovering the embedded nonlinear structure of the data set according to the observations in the highdimensional feature space. By optimizing the order information of the observations and preserving the intrinsic geometry of the data set simultaneously, the proposed algorithm provides the faithful ordinal regression to the new coming data points. To offer more general solution to the data with natural tensor structure, we further introduce the multilinear extension of the proposed algorithm, which can support the ordinal regression of high order data like images. Experiments on various data sets validate the effectiveness of the proposed algorithm as well as its extension.
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40

YAGER, RONALD R. "ORDINAL MEASURES OF SPECIFICITY." International Journal of General Systems 17, no. 1 (May 1990): 57–72. http://dx.doi.org/10.1080/03081079008935096.

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41

Shi, Yong, Peijia Li, Hao Yuan, Jianyu Miao, and Lingfeng Niu. "Fast kernel extreme learning machine for ordinal regression." Knowledge-Based Systems 177 (August 2019): 44–54. http://dx.doi.org/10.1016/j.knosys.2019.04.003.

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42

Ruan, Yu-Xun, Hsuan-Tien Lin, and Ming-Feng Tsai. "Improving ranking performance with cost-sensitive ordinal classification via regression." Information Retrieval 17, no. 1 (February 8, 2013): 1–20. http://dx.doi.org/10.1007/s10791-013-9219-2.

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43

Tastle, W. J., and M. J. Wierman. "An information theoretic measure for the evaluation of ordinal scale data." Behavior Research Methods 38, no. 3 (August 2006): 487–94. http://dx.doi.org/10.3758/bf03192803.

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44

Li, Jian, Fugee Tsung, and Changliang Zou. "A simple categorical chart for detecting location shifts with ordinal information." International Journal of Production Research 52, no. 2 (September 26, 2013): 550–62. http://dx.doi.org/10.1080/00207543.2013.838329.

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45

Sarabando, P., and L. C. Dias. "Multiattribute Choice With Ordinal Information: A Comparison of Different Decision Rules." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 39, no. 3 (May 2009): 545–54. http://dx.doi.org/10.1109/tsmca.2009.2014555.

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46

Gobel, Eric W., Daniel J. Sanchez, and Paul J. Reber. "Integration of temporal and ordinal information during serial interception sequence learning." Journal of Experimental Psychology: Learning, Memory, and Cognition 37, no. 4 (2011): 994–1000. http://dx.doi.org/10.1037/a0022959.

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47

Burns, Richard A., and Cathryn R. Criddle. "Retention of Ordinal Position Information with Limited and Extended Serial Training." Psychological Record 51, no. 3 (July 2001): 445–52. http://dx.doi.org/10.1007/bf03395408.

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48

Athanassoglou, Stergios. "Efficiency under a combination of ordinal and cardinal information on preferences." Journal of Mathematical Economics 47, no. 2 (March 2011): 180–85. http://dx.doi.org/10.1016/j.jmateco.2011.02.001.

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49

Bryant, Fred B., and Karl G. Jöreskog. "Confirmatory Factor Analysis of Ordinal Data Using Full-Information Adaptive Quadrature." Australian & New Zealand Journal of Statistics 58, no. 2 (June 2016): 173–96. http://dx.doi.org/10.1111/anzs.12154.

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50

Kemmer, Ryan, Yeawon Yoo, Adolfo Escobedo, and Ross Maciejewski. "Enhancing Collective Estimates by Aggregating Cardinal and Ordinal Inputs." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 8 (October 1, 2020): 73–82. http://dx.doi.org/10.1609/hcomp.v8i1.7465.

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There are many factors that affect the quality of data received from crowdsourcing, including cognitive biases, varying levels of expertise, and varying subjective scales. This work investigates how the elicitation and integration of multiple modalities of input can enhance the quality of collective estimations. We create a crowdsourced experiment where participants are asked to estimate the number of dots within images in two ways: ordinal (ranking) and cardinal (numerical) estimates. We run our study with 300 participants and test how the efficiency of crowdsourced computation is affected when asking participants to provide ordinal and/or cardinal inputs and how the accuracy of the aggregated outcome is affected when using a variety of aggregation methods. First, we find that more accurate ordinal and cardinal estimations can be achieved by prompting participants to provide both cardinal and ordinal information. Second, we present how accurate collective numerical estimates can be achieved with significantly fewer people when aggregating individual preferences using optimization-based consensus aggregation models. Interestingly, we also find that aggregating cardinal information may yield more accurate ordinal estimates.
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