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Journal articles on the topic 'Distance-based measure'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Wooil Kim and J. Hansen. "Phonetic Distance Based Confidence Measure." IEEE Signal Processing Letters 17, no. 2 (February 2010): 121–24. http://dx.doi.org/10.1109/lsp.2009.2034551.

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2

Sahbudin, Murtadha Arif Bin. "Audio Fingerprint based on Power Spectral Density and Hamming Distance Measure." Journal of Advanced Research in Dynamical and Control Systems 12, no. 04-Special Issue (March 31, 2020): 1533–44. http://dx.doi.org/10.5373/jardcs/v12sp4/20201633.

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3

Shen, H. C., C. Y. C. Bie, and D. K. Y. Chiu. "A texture-based distance measure for classification." Pattern Recognition 26, no. 9 (September 1993): 1429–37. http://dx.doi.org/10.1016/0031-3203(93)90148-p.

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4

Liu, Hongbing, Huaping Guo, and Chang-an Wu. "Hyperbox Granular Computing Based on Distance Measure." International Journal of Control and Automation 9, no. 1 (January 31, 2016): 1–10. http://dx.doi.org/10.14257/ijca.2016.9.1.01.

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5

Campana, Bilson J. L., and Eamonn J. Keogh. "A compression-based distance measure for texture." Statistical Analysis and Data Mining 3, no. 6 (October 7, 2010): 381–98. http://dx.doi.org/10.1002/sam.10093.

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6

Deng, Jun, and Qiujun Lu. "Fuzzy Regression Model Based on Fuzzy Distance Measure." Journal of Data Analysis and Information Processing 06, no. 03 (2018): 126–40. http://dx.doi.org/10.4236/jdaip.2018.63008.

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7

., S. Selvaraj, and K. Seetharaman. "Color Image Retrieval Based on Chernoff Distance Measure." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 329–33. http://dx.doi.org/10.26438/ijcse/v6i9.329333.

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8

OSARAGI, Toshihiro, and Ayaka MURAKAMI. "DISTANCE MEASURE BASED ON SPATIOTEMPORAL COEXISTENCE OF RESIDENTS." Journal of Architecture and Planning (Transactions of AIJ) 80, no. 715 (2015): 2001–10. http://dx.doi.org/10.3130/aija.80.2001.

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9

Tanaka, Katsuyuki, Takuji Kinkyo, and Shigeyuki Hamori. "Asymmetric technological distance measure based on language model." Applied Economics Letters 26, no. 18 (March 2019): 1548–51. http://dx.doi.org/10.1080/13504851.2019.1584364.

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10

Chen, Chao, Zhenzhou Lu, and Fei Wang. "New Global Sensitivity Measure Based on Fuzzy Distance." Journal of Engineering Mechanics 143, no. 11 (November 2017): 04017125. http://dx.doi.org/10.1061/(asce)em.1943-7889.0001336.

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11

Kim, H. K., K. C. Kim, and H. S. Lee. "Enhanced distance measure for LSP-based speech recognition." Electronics Letters 29, no. 16 (1993): 1463. http://dx.doi.org/10.1049/el:19930979.

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12

Bryce, Daniel. "Landmark-Based Plan Distance Measures for Diverse Planning." Proceedings of the International Conference on Automated Planning and Scheduling 24 (May 10, 2014): 56–64. http://dx.doi.org/10.1609/icaps.v24i1.13625.

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Prior approaches to generating diverse plans in domain-independent planning seek out variations on plan structure such as actions or causal links used, or states entered. Measuring such syntactic differences between plans can be misleading because syntactically different plans can be semantically identical. We develop a landmark-based plan distance measure that captures semantic differences between plans.The landmark-based distance measure focuses on the disjunctive landmarks satisfied by each plan. We develop a simple algorithm for finding diverse plans that is based upon the LAMA planner. We illustrate that, in comparison with plan distance measures, landmark-based plan distance is not as susceptible to including irrelevant or redundant actions in plans to increase plan distance. Through extensive empirical evaluation, we find that high landmark distance between plans implies high action set distance, but not vice versa. Landmark-based plan distance overcomes some of the weaknesses of syntactic plan distance measures and can be used to find plan sets that are both landmark diverse and action set diverse.
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13

Forsgren, P. O., and P. Seideman. "An interobject distance measure based on medial axes retrieved from discrete distance maps." IEEE Transactions on Pattern Analysis and Machine Intelligence 12, no. 4 (April 1990): 390–97. http://dx.doi.org/10.1109/34.50624.

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14

Tang, Hui-Chin, and Shen-Tai Yang. "Counterintuitive Test Problems for Distance-Based Similarity Measures Between Intuitionistic Fuzzy Sets." Mathematics 7, no. 5 (May 17, 2019): 437. http://dx.doi.org/10.3390/math7050437.

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This paper analyzes the counterintuitive behaviors of adopted twelve distance-based similarity measures between intuitionistic fuzzy sets. Among these distance-based similarity measures, the largest number of components of the distance in the similarity measure is four. We propose six general counterintuitive test problems to analyze their counterintuitive behaviors. The results indicate that all the distance-based similarity measures have some counterintuitive test problems. Furthermore, for the largest number of components of the distance-based similarity measure, four types of counterintuitive examples exist. Therefore, the counterintuitive behaviors are inevitable for the distance-based similarity measures between intuitionistic fuzzy sets.
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15

Li, Mu, Qilong Wang, David Zhang, Peihua Li, and Wangmeng Zuo. "Joint distance and similarity measure learning based on triplet-based constraints." Information Sciences 406-407 (September 2017): 119–32. http://dx.doi.org/10.1016/j.ins.2017.04.027.

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16

El Hindi, Khalil, Bayan Abu Shawar, Reem Aljulaidan, and Hussien Alsalamn. "Improved Distance Functions for Instance-Based Text Classification." Computational Intelligence and Neuroscience 2020 (November 22, 2020): 1–10. http://dx.doi.org/10.1155/2020/4717984.

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Text classification has many applications in text processing and information retrieval. Instance-based learning (IBL) is among the top-performing text classification methods. However, its effectiveness depends on the distance function it uses to determine similar documents. In this study, we evaluate some popular distance measures’ performance and propose new ones that exploit word frequencies and the ordinal relationship between them. In particular, we propose new distance measures that are based on the value distance metric (VDM) and the inverted specific-class distance measure (ISCDM). The proposed measures are suitable for documents represented as vectors of word frequencies. We compare these measures’ performance with their original counterparts and with powerful Naïve Bayesian-based text classification algorithms. We evaluate the proposed distance measures using the kNN algorithm on 18 benchmark text classification datasets. Our empirical results reveal that the distance metrics for nominal values render better classification results for text classification than the Euclidean distance measure for numeric values. Furthermore, our results indicate that ISCDM substantially outperforms VDM, but it is also more susceptible to make use of the ordinal nature of term-frequencies than VDM. Thus, we were able to propose more ISCDM-based distance measures for text classification than VDM-based measures. We also compare the proposed distance measures with Naïve Bayesian-based text classification, namely, multinomial Naïve Bayes (MNB), complement Naïve Bayes (CNB), and the one-versus-all-but-one (OVA) model. It turned out that when kNN uses some of the proposed measures, it outperforms NB-based text classifiers for most datasets.
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17

Qiang, He Qun, Chun Hua Qian, and Sheng Rong Gong. "Similarity Measure for Image Retrieval Based on Hausdorff Distance." Applied Mechanics and Materials 635-637 (September 2014): 1039–44. http://dx.doi.org/10.4028/www.scientific.net/amm.635-637.1039.

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In general, it is difficult to segment accurately image regions or boundary contours and represent them by feature vectors for shape-based image query. Therefore, the object similarity is often computed by their boundaries. Hausdorff distance is nonlinear for computing distance, it can be used to measure the similarity between two patterns of points of edge images. Classical Hausdorff measure need to express image as a feature matrix firstly, then calculate feature values or feature vectors, so it is time-consuming. Otherwise, it is difficult for part pattern matching when shadow and noise existed. In this paper, an algorithm that use Hausdorff distance on the image boundaries to measure similarity is proposed. Experimental result has showed that the proposed algorithm is robust.
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18

Garcia-Garcia, D., E. P. Hernandez, and F. Diaz de Maria. "A New Distance Measure for Model-Based Sequence Clustering." IEEE Transactions on Pattern Analysis and Machine Intelligence 31, no. 7 (July 2009): 1325–31. http://dx.doi.org/10.1109/tpami.2008.268.

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19

Paliwal, K. K. "A perception‐based LSP distance measure for speech recognition." Journal of the Acoustical Society of America 84, S1 (November 1988): S14—S15. http://dx.doi.org/10.1121/1.2025867.

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20

Zhang, Yonghong, Hong'an Wu, Huiqin Wang, and Shanshan Jin. "Distance Measure Based Change Detectors for Polarimetric SAR Imagery." Photogrammetric Engineering & Remote Sensing 82, no. 9 (September 1, 2016): 719–27. http://dx.doi.org/10.14358/pers.82.9.719.

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21

Schneider, A., G. H. Gonnet, and G. M. Cannarozzi. "SynPAM—A Distance Measure Based on Synonymous Codon Substitutions." IEEE/ACM Transactions on Computational Biology and Bioinformatics 4, no. 4 (October 2007): 553–60. http://dx.doi.org/10.1109/tcbb.2007.1071.

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22

Li, Yan, Pan Su, and Wenliang Li. "A Game Map Complexity Measure Based on Hamming Distance." Physics Procedia 22 (2011): 634–40. http://dx.doi.org/10.1016/j.phpro.2011.11.098.

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23

González-Arteaga, T., J. C. R. Alcantud, and R. de Andrés Calle. "A cardinal dissensus measure based on the Mahalanobis distance." European Journal of Operational Research 251, no. 2 (June 2016): 575–85. http://dx.doi.org/10.1016/j.ejor.2015.11.019.

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24

Wang, Xiao, Fusheng Yu, and Witold Pedrycz. "An area-based shape distance measure of time series." Applied Soft Computing 48 (November 2016): 650–59. http://dx.doi.org/10.1016/j.asoc.2016.06.033.

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25

Zhang, Hailong, and Yongbin Zhou. "Mahalanobis Distance Similarity Measure Based Higher Order Optimal Distinguisher." Computer Journal 60, no. 8 (February 2, 2017): 1131–44. http://dx.doi.org/10.1093/comjnl/bxw093.

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26

Zhang, Hailong, Yongbin Zhou, and Dengguo Feng. "Mahalanobis distance similarity measure based distinguisher for template attack." Security and Communication Networks 8, no. 5 (May 5, 2014): 769–77. http://dx.doi.org/10.1002/sec.1033.

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27

Yin, Longjun, Qinghua Zhang, Fan Zhao, Qiong Mou, and Sidong Xian. "A new distance measure for pythagorean fuzzy sets based on earth mover’s distance and its applications." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 3079–92. http://dx.doi.org/10.3233/jifs-210800.

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In uncertain information processing, new knowledge can be discovered by measuring the proximity between discovered and undiscovered knowledge. Pythagorean Fuzzy Sets (PFSs) is one of the important tools to describe the natural attributes of uncertain information. Therefore, how to appropriately measure the distance between PFSs is an important topic. The earth mover’s distance (EMD) is a real distance metric that can be used to describe the difference between two distribution laws. In this paper, a new distance measure for PFSs based on EMD is proposed. It is a new perspective to measure the distance between PFSs from the perspective of distribution law. First, a new distance measure namely DEMD is presented and proven to satisfy the distance measurement axiom. Second, an example is given to illustrate the advantages of DEMD compared with other distance measures. Third, the problem statements and solving algorithms of pattern recognition, medical diagnosis and multi-criteria decision making (MCDM) problems are given. Finally, by comparing the application of different methods in pattern recognition, medical diagnosis and MCDM, the effectiveness and practicability of DEMD and algorithms presented in this paper are demonstrated.
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28

Sulaiman, Nor Hashimah, Daud Mohamad, Jamilah Mohd Shariff, Sharifah Aniza Sayed Ahmad, and Kamilah Abdullah. "Extended FTOPSIS with Distance and Set Theoretic-Based Similarity Measure." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (February 1, 2018): 387. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp387-394.

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Comparing fuzzy numbers is an essential process in deducing the output of many fuzzy decision making methods. One of the comparison methods commonly used is by using similarity measure. The main advantage of the similarity measure over other approaches is its ability to minimize the loss of information in the computational process. Several similarity measures have been applied effectively in fuzzy decision making methods. In this paper, a new similarity measure based on the geometric distance, the center of gravity, Hausdorf distance and the set theoretic similarity formula known as the Dice similarity index are incorporated into the Extended Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) method particularly in calculating the closeness coefficients. This similarity measure is in favor of others as it is able to discriminate two similar shape fuzzy numbers effectively with two different locations. A validation process is carried out by implementing the proposed procedure of the Extended FTOPSIS with the new similarity measure in solving a supplier selection problem and the ranking outcome is then compared with the Extended FTOPSIS with other existing similarity measure. The result shows that the Extended FTOPSIS with the proposed similarity measure gives a consistent result without reducing any information in the computational process.
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29

Xing, Xiaoli, Qihao Chen, and Xiuguo Liu. "HETEROGENEITY MEASUREMENT BASED ON DISTANCE MEASURE FOR POLARIMETRIC SAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3 (April 23, 2018): 233–37. http://dx.doi.org/10.5194/isprs-annals-iv-3-233-2018.

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To effectively test the scene heterogeneity for polarimetric synthetic aperture radar (PolSAR) data, in this paper, the distance measure is introduced by utilizing the similarity between the sample and pixels. Moreover, given the influence of the distribution and modeling texture, the K distance measure is deduced according to the Wishart distance measure. Specifically, the average of the pixels in the local window replaces the class center coherency or covariance matrix. The Wishart and K distance measure are calculated between the average matrix and the pixels. Then, the ratio of the standard deviation to the mean is established for the Wishart and K distance measure, and the two features are defined and applied to reflect the complexity of the scene. The proposed heterogeneity measure is proceeded by integrating the two features using the Pauli basis. The experiments conducted on the single–look and multilook PolSAR data demonstrate the effectiveness of the proposed method for the detection of the scene heterogeneity.
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30

Singh, Pushpinder. "Some new distance measures for type-2 fuzzy sets and distance measure based ranking for group decision making problems." Frontiers of Computer Science 8, no. 5 (August 26, 2014): 741–52. http://dx.doi.org/10.1007/s11704-014-3323-3.

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31

Li, Yan, Wen Ju Zhao, and Zhen Hua Zhou. "A Map Complexity Measure Based on Contact Surface." Applied Mechanics and Materials 411-414 (September 2013): 1994–97. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1994.

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This paper defined the full connect map and contact surface, and proposed a new map complexity measure, and compared with measurement methods based on Hamming distance and relative Hamming distance. We further research on the relationship between the complexity measure and the map connectivity. The complexity measures based on Hamming distance and contact surface are applicable to full connectivity map, and the new measurement can reflects the difficulty of the pathfinding algorithm more accurately, especially in a higher complexity.
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32

Cui, Wen-Hua, and Jun Ye. "Generalized Distance-Based Entropy and Dimension Root Entropy for Simplified Neutrosophic Sets." Entropy 20, no. 11 (November 4, 2018): 844. http://dx.doi.org/10.3390/e20110844.

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In order to quantify the fuzziness in the simplified neutrosophic setting, this paper proposes a generalized distance-based entropy measure and a dimension root entropy measure of simplified neutrosophic sets (NSs) (containing interval-valued and single-valued NSs) and verifies their properties. Then, comparison with the existing relative interval-valued NS entropy measures through a numerical example is carried out to demonstrate the feasibility and rationality of the presented generalized distance-based entropy and dimension root entropy measures of simplified NSs. Lastly, a decision-making example is presented to illustrate their applicability, and then the decision results indicate that the presented entropy measures are effective and reasonable. Hence, this study enriches the simplified neutrosophic entropy theory and measure approaches.
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33

Wang, Xiao Fen, Hai Na Zhang, Xiu Rong Qiu, Jiang Ping Song, and Ke Xin Zhang. "Image Retrieval of Self-Adapt Distance Measure Based on SLLE." Advanced Materials Research 989-994 (July 2014): 3675–78. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.3675.

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Self-adapt distance measure supervised locally linear embedding solves the problem that Euclidean distance measure can not apart from samples in content-based image retrieval. This method uses discriminative distance measure to construct k-NN and effectively keeps its topological structure in high dimension space, meanwhile it broadens interval of samples and strengthens the ability of classifying. Experiment results show the ADM-SLLE date-reducing-dimension method speeds up the image retrieval and acquires high accurate rate in retrieval.
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34

Jeon, Heonbae, Soonbong Lee, Hongseon Kim, Seung Bum Soh, and Seongmoon Kim. "Portfolio Evaluation with the Vector Distance Based on Portfolio Composition." Mathematics 11, no. 1 (January 1, 2023): 221. http://dx.doi.org/10.3390/math11010221.

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We propose a novel portfolio evaluation method, a distance-based approach, which directly evaluates the portfolio composition rather than portfolio returns. In this approach, we consider a portfolio as an estimator for an in-sample tangency portfolio, which we define as the optimal reference portfolio. We then evaluate the portfolio by computing its vector distance to the optimal reference portfolio. In search of the proper distance-based performance measure, we choose four representative vector distances and compare their suitability as a new portfolio performance measure. Through extensive statistical analysis, we find that the Euclidean distance is the most proper distance-based performance measure of the four representative vector distances. We further verify that a portfolio with a large Euclidean distance is not desirable because not only does it provide a low utility implied by the first four moments of portfolio returns, but also it is not likely to maintain its long-term performance. Hence, the Euclidean distance can complement the return-based performance measures by confirming the reliability of a portfolio in its investment performance.
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35

Zeng, Yanqiu, Haiping Ren, Tonghua Yang, Shixiao Xiao, and Neal Xiong. "A Novel Similarity Measure of Single-Valued Neutrosophic Sets Based on Modified Manhattan Distance and Its Applications." Electronics 11, no. 6 (March 17, 2022): 941. http://dx.doi.org/10.3390/electronics11060941.

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A single-valued neutrosophic (SVN) set contains three parameters, which can well describe three aspects of an objective thing. However, most previous similarity measures of SVN sets often encounter some counter-intuitive examples. Manhattan distance is a well-known distance, which has been applied in pattern recognition, image analysis, ad-hoc wireless sensor networks, etc. In order to develop suitable distance measures, a new distance measure of SVN sets based on modified Manhattan distance is constructed, and a new distance-based similarity measure also is put forward. Then some applications of the proposed similarity measure are introduced. First, we introduce a pattern recognition algorithm. Then a multi-attribute decision-making method is proposed, in which a weighting method is developed by building an optimal model based on the proposed similarity measure. Furthermore, a clustering algorithm is also put forward. Some examples are also used to illustrate these methods.
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36

XU, BAOMIN, TINGLIN XIN, YUNFENG WANG, and YANPIN ZHAO. "LOCAL RANDOM WALK WITH DISTANCE MEASURE." Modern Physics Letters B 27, no. 08 (March 13, 2013): 1350055. http://dx.doi.org/10.1142/s0217984913500553.

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Link prediction based on random walks has been widely used. The existing random walk algorithms ignore the probability of a walker visit from the initial node to the destination node for the first time, which makes a major contribution to establish links in some networks. To deal with the problem, we develop a link prediction method named Local Random Walk with Distance (LRWD) based on local random walk and the shortest distance of node pairs. In LRWD, walkers walk with their own steps rather than uniform steps. To evaluate the performance of the LRWD algorithm, we present the concept of distance distribution. The experimental results show that LRWD can improve the prediction accuracy when the distance distribution of the network is relatively concentrated.
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37

Peng, Bo, Chunming Ye, and Shouzhen Zeng. "Some Intuitionist Fuzzy Weighted Geometric Distance Measures and Their Application to Group Decision Making." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 22, no. 05 (October 2014): 699–715. http://dx.doi.org/10.1142/s0218488514500354.

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The ordered weighted distance (OWD) measure developed by Xu and Chen having been proved suitable to deal with the situation where the input arguments are represented in exact numerical values. In this paper, we develop some new geometric distance measures with intuitionistic fuzzy information, which are the generalization of some widely used distance measures, including the intuitionistic fuzzy weighted geometric distance (IFWGD) measure, the intuitionistic fuzzy ordered weighted geometric distance (IFOWGD) measure, the intuitionistic fuzzy ordered weighted geometric Hamming distance (IFOWGHD) measure, the intuitionistic fuzzy ordered weighted geometric Euclidean distance (IFOWGED) measure, the intuitionistic fuzzy hybrid weighted geometric distance (IFHWGD) measure. These developed weighted geometric distance measures are very suitable to deal with the situation where the input arguments are represented in intuitionistic fuzzy values. And then, we present a consensus reaching process based on the developed distance measures with intuitionistic fuzzy preference information for group decision making. Finally, we apply the developed approach with a numerical example to group decision making under intuitionistic fuzzy environment.
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38

Hernandez, Y. Rosales, and T. Hiyama. "Distance Measure Based Rules for Voltage Regulation with Loss Reduction." Journal of Electromagnetic Analysis and Applications 01, no. 02 (2009): 85–91. http://dx.doi.org/10.4236/jemaa.2009.12013.

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39

Mishra, Kakuli, Srinka Basu, and Ujjwal Maulik. "SeqDTW: A Segmentation Based Distance Measure for Time Series Data." Transactions of the Indian National Academy of Engineering 6, no. 3 (May 15, 2021): 709–30. http://dx.doi.org/10.1007/s41403-021-00230-1.

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40

Wu, G., S. Wang, Y. Dong, and B. Wang. "Multi-hop distance estimation method based on regulated neighbourhood measure." IET Communications 6, no. 13 (September 5, 2012): 2084–90. http://dx.doi.org/10.1049/iet-com.2012.0108.

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41

Lagraa, Sofiane, Hamida Seba, Riadh Khennoufa, Abir M׳Baya, and Hamamache Kheddouci. "A distance measure for large graphs based on prime graphs." Pattern Recognition 47, no. 9 (September 2014): 2993–3005. http://dx.doi.org/10.1016/j.patcog.2014.03.014.

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42

G, Poornalatha, and Prakash Raghavendra. "Alignment Based Similarity distance Measure for Better Web Sessions Clustering." Procedia Computer Science 5 (2011): 450–57. http://dx.doi.org/10.1016/j.procs.2011.07.058.

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43

Wang, Jun, and Xiaoqi Zheng. "WSE, a new sequence distance measure based on word frequencies." Mathematical Biosciences 215, no. 1 (September 2008): 78–83. http://dx.doi.org/10.1016/j.mbs.2008.06.001.

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44

M�ntaras, R. L�pez. "A distance-based attribute selection measure for decision tree induction." Machine Learning 6, no. 1 (January 1991): 81–92. http://dx.doi.org/10.1007/bf00153761.

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45

Wan, Xiaojun. "A novel document similarity measure based on earth mover’s distance." Information Sciences 177, no. 18 (September 2007): 3718–30. http://dx.doi.org/10.1016/j.ins.2007.02.045.

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46

Baudrier, E., G. Millon, F. Nicolier, R. Seulin, and S. Ruan. "Hausdorff distance-based multiresolution maps applied to image similarity measure." Imaging Science Journal 55, no. 3 (September 2007): 164–74. http://dx.doi.org/10.1179/174313107x166884.

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47

Ghorbani, Modjtaba, Matthias Dehmer, Mina Rajabi-Parsa, Abbe Mowshowitz, and Frank Emmert-Streib. "On Properties of Distance-Based Entropies on Fullerene Graphs." Entropy 21, no. 5 (May 10, 2019): 482. http://dx.doi.org/10.3390/e21050482.

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In this paper, we study several distance-based entropy measures on fullerene graphs. These include the topological information content of a graph I a ( G ) , a degree-based entropy measure, the eccentric-entropy I f σ ( G ) , the Hosoya entropy H ( G ) and, finally, the radial centric information entropy H e c c . We compare these measures on two infinite classes of fullerene graphs denoted by A 12 n + 4 and B 12 n + 6 . We have chosen these measures as they are easily computable and capture meaningful graph properties. To demonstrate the utility of these measures, we investigate the Pearson correlation between them on the fullerene graphs.
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48

Li, Wei, and Shouzhen Zeng. "Uncertain Linguistic Aggregation Distance Measures and Their Application to Group Decision Making." Journal of Applied Mathematics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/563650.

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We introduce a method based on distance measures for group decision making under uncertain linguistic environment. We develop some uncertain linguistic aggregation distance measures called the uncertain linguistic weighted distance (ULWD) measure, the uncertain linguistic ordered weighted distance (ULOWD) measure, and the uncertain linguistic hybrid weighted distance (ULHWD) measure. We study some of their characteristic, and we prove that the ULWD and the ULOWD are special cases of the ULHWD measure. Finally, we develop an application of the ULHWD measure in a group decision making problem concerning the evaluation of university faculty for tenure and promotion with uncertain linguistic information.
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49

Wu, Xuan, Yafei Song, and Yifei Wang. "Distance-Based Knowledge Measure for Intuitionistic Fuzzy Sets with Its Application in Decision Making." Entropy 23, no. 9 (August 28, 2021): 1119. http://dx.doi.org/10.3390/e23091119.

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Much attention has been paid to construct an applicable knowledge measure or uncertainty measure for Atanassov’s intuitionistic fuzzy set (AIFS). However, many of these measures were developed from intuitionistic fuzzy entropy, which cannot really reflect the knowledge amount associated with an AIFS well. Some knowledge measures were constructed based on the distinction between an AIFS and its complementary set, which may lead to information loss in decision making. In this paper, knowledge amount of an AIFS is quantified by calculating the distance from an AIFS to the AIFS with maximum uncertainty. Axiomatic properties for the definition of knowledge measure are extended to a more general level. Then the new knowledge measure is developed based on an intuitionistic fuzzy distance measure. The properties of the proposed distance-based knowledge measure are investigated based on mathematical analysis and numerical examples. The proposed knowledge measure is finally applied to solve the multi-attribute group decision-making (MAGDM) problem with intuitionistic fuzzy information. The new MAGDM method is used to evaluate the threat level of malicious code. Experimental results in malicious code threat evaluation demonstrate the effectiveness and validity of proposed method.
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Naranjo, Rodrigo, Matilde Santos, and Luis Garmendia. "A convolution-based distance measure for fuzzy singletons and its application in a pattern recognition problem." Integrated Computer-Aided Engineering 28, no. 1 (December 21, 2020): 51–63. http://dx.doi.org/10.3233/ica-200629.

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A new method to measure the distance between fuzzy singletons (FSNs) is presented. It first fuzzifies a crisp number to a generalized trapezoidal fuzzy number (GTFN) using the Mamdani fuzzification method. It then treats an FSN as an impulse signal and transforms the FSN into a new GTFN by convoluting it with the original GTFN. In so doing, an existing distance measure for GTFNs can be used to measure distance between FSNs. It is shown that the new measure offers a desirable behavior over the Euclidean and weighted distance measures in the following sense: Under the new measure, the distance between two FSNs is larger when they are in different GTFNs, and smaller when they are in the same GTFN. The advantage of the new measure is demonstrated on a fuzzy forecasting trading system over two different real stock markets, which provides better predictions with larger profits than those obtained using the Euclidean distance measure for the same system.
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