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Journal articles on the topic 'Ordinal information'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

Yager, Ronald R. "Aggregation of ordinal information." Fuzzy Optimization and Decision Making 6, no. 3 (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 (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 (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 (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 (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 (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 (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 (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

KELLER, KARSTEN, MATHIEU SINN, and JAN EMONDS. "TIME SERIES FROM THE ORDINAL VIEWPOINT." Stochastics and Dynamics 07, no. 02 (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 i

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12

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 (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

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13

Du Plessis, Phillip J., and Michael J. Greenacre. "Modelling Information Search Behaviour of Car Purchasers." South African Journal of Psychology 19, no. 3 (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 appropriat

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14

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

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15

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

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16

YAGER, RONALD R. "DECISION MAKING UNDER UNCERTAINTY WITH ORDINAL INFORMATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 07, no. 05 (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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17

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

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18

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

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19

Xu, Xinzheng, Qiaoyu Guo, Zhongnian Li, and Dechun Li. "Uncertainty Ordinal Multi-Instance Learning for Breast Cancer Diagnosis." Healthcare 10, no. 11 (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 lesi

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20

O'Reilly, Jill X., Katharine J. McCarthy, Mariagrazia Capizzi, and Anna Christina Nobre. "Acquisition of the Temporal and Ordinal Structure of Movement Sequences in Incidental Learning." Journal of Neurophysiology 99, no. 5 (2008): 2731–35. http://dx.doi.org/10.1152/jn.01141.2007.

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We investigated the acquisition and integration of temporal and ordinal sequence information in an incidental learning model of motor skill acquisition (the serial reaction time task). Human participants were exposed to a stimulus-response sequence that had temporal structure, ordinal structure, or both. By changing the temporal or ordinal structure, or both, we were able to ask two questions: first, does a regular temporal structure facilitate learning of an ordinal sequence and second, is a temporal sequence, presented in the context of a random ordinal sequence of finger movements, “picked

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21

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

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22

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

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23

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

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24

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

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25

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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26

González del Pozo, Raquel, Luis C. Dias, and José Luis García-Lapresta. "Using Different Qualitative Scales in a Multi-Criteria Decision-Making Procedure." Mathematics 8, no. 3 (2020): 458. http://dx.doi.org/10.3390/math8030458.

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Many decision problems manage linguistic information assessed through several ordered qualitative scales. In these contexts, the main problem arising is how to aggregate this qualitative information. In this paper, we present a multi-criteria decision-making procedure that ranks a set of alternatives assessed by means of a specific ordered qualitative scale for each criterion. These ordered qualitative scales can be non-uniform and be formed by a different number of linguistic terms. The proposed procedure follows an ordinal approach by means of the notion of ordinal proximity measure that ass

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27

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 wh

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28

Lehnertz, Klaus. "Ordinal methods for a characterization of evolving functional brain networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 2 (2023): 022101. http://dx.doi.org/10.1063/5.0136181.

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Ordinal time series analysis is based on the idea to map time series to ordinal patterns, i.e., order relations between the values of a time series and not the values themselves, as introduced in 2002 by C. Bandt and B. Pompe. Despite a resulting loss of information, this approach captures meaningful information about the temporal structure of the underlying system dynamics as well as about properties of interactions between coupled systems. This—together with its conceptual simplicity and robustness against measurement noise—makes ordinal time series analysis well suited to improve characteri

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29

Alvandi, Amirhossein, and Armin Hatefi. "Estimation of ordinal population with multi-observer ranked set samples using ties information." Statistical Methods in Medical Research 30, no. 8 (2021): 1960–75. http://dx.doi.org/10.1177/09622802211025989.

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In many surveys, we often deal with situations where measuring the study variable is expensive; however, there are easy-to-measure characteristics which can be used as ranking information to obtain more representative samples from the population. Ranked set sampling is successfully employed in these cases as an alternative to commonly used simple random sampling. When the data is ordinal categorical, it is common to apply the ordinal logistic regression approach to ranked set sampling data for the estimation of parameters. This technique first depends on the information of training data. Besid

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30

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

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31

Bagai, Rajiv, Marc Bezem, and M. H. van Emden. "On Downward Closure Ordinals of Logic Programs." Fundamenta Informaticae 13, no. 1 (1990): 67–83. http://dx.doi.org/10.3233/fi-1990-13107.

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Blair has shown that for every ordinal up to and including the least non-recursive ordinal there exists a logic program having that ordinal as downward closure ordinal. However, given such an ordinal and Blair’s proof, it is not straightforward to find a corresponding logic program. In fact, in the literature only a few isolated, ad hoc, examples of logic programs with downward closure ordinal greater than w can be found. We contribute to bridging the gap between what is known abstractly and what is known concretely by showing the connection between some of the existing examples and the well-k

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32

Mondonneix, Gaël, Jean Martial Mari, Sébastien Chabrier, and Alban Gabillon. "A kernel machine for hidden object-ranking problems (HORPs)." Multimedia Tools and Applications 79, no. 47-48 (2020): 35093–107. http://dx.doi.org/10.1007/s11042-020-09184-y.

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AbstractHidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clu

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33

Chergui, Zhor, and Antonio Jiménez-Martín. "On Ordinal Information-Based Weighting Methods and Comparison Analyses." Information 15, no. 9 (2024): 527. http://dx.doi.org/10.3390/info15090527.

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In this paper, we focus on weighting methods within multi-attribute utility/value theory (MAUT/MAVT). In these methods, the decision maker (DM) provides ordinal information about the relative importance of criteria, but also additional information concerning the strength of the differences between the ranked criteria, which can be expressed in different forms, including precise/imprecise cardinal information, ratio-based methods, a ranking of differences, a semantic scale, or preference statements. Although many comparison analyses of weighting methods based on ordinal information have been ca

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34

HERRERA-VIEDMA, E., A. G. LÓPEZ-HERRERA, M. LUQUE, and C. PORCEL. "A FUZZY LINGUISTIC IRS MODEL BASED ON A 2-TUPLE FUZZY LINGUISTIC APPROACH." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 15, no. 02 (2007): 225–50. http://dx.doi.org/10.1142/s0218488507004534.

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Information Retrieval Systems (IRSs) based on an ordinal fuzzy linguistic approach present some problems of loss of information and lack of precision when working with discrete linguistic expression domains or when applying approximation operations in the symbolic aggregation methods. In this paper, we present a new IRS model based on the 2-tuple fuzzy linguistic approach, which allows us to overcome the problems of ordinal fuzzy linguistic IRSs and improve their performance.

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35

BROUWER, ROELOF K. "FUZZY CLUSTERING OF FEATURE VECTORS WITH SOME ORDINAL VALUED ATTRIBUTES USING GRADIENT DESCENT FOR LEARNING." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 02 (2008): 195–218. http://dx.doi.org/10.1142/s0218488508005133.

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There are well established methods for fuzzy clustering especially for the cases where the feature values are numerical of ratio or interval scale. Not so well established are methods to be applied when the feature values are ordinal or nominal. In that case there is no one best method it seems. This paper discusses a method where unknown numeric variables are assigned to the ordinal values. Part of minimizing an objective function for the clustering is to find numeric values for these variables. Thus real numbers of interval scale and even ratio scale for that matter are assigned to the origi

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36

Nüßgen, Ines, and Alexander Schnurr. "Ordinal Pattern Dependence in the Context of Long-Range Dependence." Entropy 23, no. 6 (2021): 670. http://dx.doi.org/10.3390/e23060670.

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Ordinal pattern dependence is a multivariate dependence measure based on the co-movement of two time series. In strong connection to ordinal time series analysis, the ordinal information is taken into account to derive robust results on the dependence between the two processes. This article deals with ordinal pattern dependence for a long-range dependent time series including mixed cases of short- and long-range dependence. We investigate the limit distributions for estimators of ordinal pattern dependence. In doing so, we point out the differences that arise for the underlying time series hav

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37

Rutte, Christel G., Kristina A. Diekmann, Jeffrey T. Polzer, Faye J. Crosby, and David M. Messick. "Organization of Information and the Detection of Gender Discrimination." Psychological Science 5, no. 4 (1994): 226–31. http://dx.doi.org/10.1111/j.1467-9280.1994.tb00505.x.

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This report examines several factors that influence the detection of gender discrimination in organizations We presented subjects with information about the qualifications and salaries of women and men in 10 departments of a hypothetical company The information was created so that one or the other gender was undercompensated relative to its qualifications Subjects rated the fairness of salaries and recalled the average qualifications of the women and men Ordinal inequity, which occurred when the gender with the better qualifications received a lower salary within a department, was easily detec

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38

Chu, Wei, and S. Sathiya Keerthi. "Support Vector Ordinal Regression." Neural Computation 19, no. 3 (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 expe

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39

Henry, Miguel, and George Judge. "Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series." Econometrics 7, no. 1 (2019): 10. http://dx.doi.org/10.3390/econometrics7010010.

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The focus of this paper is an information theoretic-symbolic logic approach to extract information from complex economic systems and unlock its dynamic content. Permutation Entropy (PE) is used to capture the permutation patterns-ordinal relations among the individual values of a given time series; to obtain a probability distribution of the accessible patterns; and to quantify the degree of complexity of an economic behavior system. Ordinal patterns are used to describe the intrinsic patterns, which are hidden in the dynamics of the economic system. Empirical applications involving the Dow Jo

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40

Zanin, Massimiliano. "Continuous ordinal patterns: Creating a bridge between ordinal analysis and deep learning." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 3 (2023): 033114. http://dx.doi.org/10.1063/5.0136492.

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We introduce a generalization of the celebrated ordinal pattern approach for the analysis of time series, in which these are evaluated in terms of their distance to ordinal patterns defined in a continuous way. This allows us to naturally incorporate information about the local amplitude of the data and to optimize the ordinal pattern(s) to the problem under study. This last element represents a novel bridge between standard ordinal analysis and deep learning, allowing the achievement of results comparable to the latter in real-world classification problems while also retaining the conceptual

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41

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

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42

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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43

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 (2015): 2522–34. http://dx.doi.org/10.1109/tcyb.2014.2376517.

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44

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

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45

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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46

Au, Domenic, Robert S. Allison, and Laurie M. Wilcox. "Conflicting ordinal depth information interferes with visually-guided reaching." Journal of Vision 23, no. 9 (2023): 5154. http://dx.doi.org/10.1167/jov.23.9.5154.

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47

Burkhardt, Jakob, Ioannis Caragiannis, Karl Fehrs, Matteo Russo, Chris Schwiegelshohn, and Sudarshan Shyam. "Low-Distortion Clustering with Ordinal and Limited Cardinal Information." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (2024): 9555–63. http://dx.doi.org/10.1609/aaai.v38i9.28811.

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Motivated by recent work in computational social choice, we extend the metric distortion framework to clustering problems. Given a set of n agents located in an underlying metric space, our goal is to partition them into k clusters, optimizing some social cost objective. The metric space is defined by a distance function d between the agent locations. Information about d is available only implicitly via n rankings, through which each agent ranks all other agents in terms of their distance from her. Still, even though no cardinal information (i.e., the exact distance values) is available, we wo

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48

Zhang, Dong Ling, Xiu Zhi Zhu, and Jie Qun Hua. "Internal Control Quality Evaluation Model for Information System Based on QFD and Risk Analysis." Applied Mechanics and Materials 643 (September 2014): 380–84. http://dx.doi.org/10.4028/www.scientific.net/amm.643.380.

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Internal control Quality evaluation of the information system based on the risk management is a more significant issue in the corporate governance. It presents a proposed method for the development of quality assessment and risk evaluation and early-warning for internal control of the information system, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: processing a quality design through QFD based on the customer need, building a quality evaluation index system of internal control based on risk analysis, building the ordinal logi

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49

HERRERA-VIEDMA, E. "AN INFORMATION RETRIEVAL MODEL WITH ORDINAL LINGUISTIC WEIGHTED QUERIES BASED ON TWO WEIGHTING ELEMENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 09, supp01 (2001): 77–87. http://dx.doi.org/10.1142/s0218488501001009.

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An Information Retrieval (IR) model defined using an ordinal fuzzy linguistic approach is proposed. It accepts ordinal linguistic weighted queries based on two weighting elements: the query terms and the query sub-expressions. In such a way, users may easily express simultaneously several semantic restrictions in a query. A symmetrical threshold semantic is associated to the weights of the query terms and an importance semantic is associated to the weights of the query sub-expressions. The advantage of this IR model with respect to others is the facility for expressing different semantic restr

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50

Mu, Peng Fei, Dong Ling Zhang, Xiao Mei Xu, and Yang Liu. "Quality Classification Model of Material Products Based on Ordinal Logistic Regression." Advanced Materials Research 460 (February 2012): 393–97. http://dx.doi.org/10.4028/www.scientific.net/amr.460.393.

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It presents a proposed method for the development of quality evaluation and classification for material products, and shows the application of the ordinal logistic regression model and its advantages. It involved several steps: applying the linguistic information processing method, building the ordinal logistic regression model, differentiating and analyzing the quality evaluation to reach the quality classification result

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