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Journal articles on the topic 'One-class'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 March 2025

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1

Levchuk, V. N. "On One Class of Non-Dissipative Operators." Zurnal matematiceskoj fiziki, analiza, geometrii 13, no. 2 (June 25, 2017): 173–94. http://dx.doi.org/10.15407/mag13.02.173.

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2

Nohel, John A. "A class of one-dimensional degenerate parabolic equations." Časopis pro pěstování matematiky 111, no. 3 (1986): 294–303. http://dx.doi.org/10.21136/cpm.1986.108153.

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3

Hoffman, D. G., and C. A. Rodger. "Class one graphs." Journal of Combinatorial Theory, Series B 44, no. 3 (June 1988): 372–76. http://dx.doi.org/10.1016/0095-8956(88)90046-9.

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4

Brereton, Richard G. "One-class classifiers." Journal of Chemometrics 25, no. 5 (May 2011): 225–46. http://dx.doi.org/10.1002/cem.1397.

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5

Deng, Xueqing, Wenkai Li, Xiaoping Liu, Qinghua Guo, and Shawn Newsam. "One-class remote sensing classification: one-class vs. binary classifiers." International Journal of Remote Sensing 39, no. 6 (January 3, 2018): 1890–910. http://dx.doi.org/10.1080/01431161.2017.1416697.

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6

Shcherbakov, E. A., and I. A. Avdeyev. "ON THE COMPACTNESS OF ONE CLASS OF QUASICONFORMAL MAPPINGS." Issues of Analysis 26, no. 3 (November 2019): 147–51. http://dx.doi.org/10.15393/j3.art.2019.6670.

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7

Minassian, D. P. "Baire Class One: 10999." American Mathematical Monthly 112, no. 2 (February 1, 2005): 181. http://dx.doi.org/10.2307/30037425.

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8

Wang, Defeng, Daniel S. Yeung, and Eric C. C. Tsang. "Structured One-Class Classification." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 36, no. 6 (December 2006): 1283–95. http://dx.doi.org/10.1109/tsmcb.2006.876189.

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9

Roth, Wolff-Michael, Sylvie Boutonné, Campbell J. McRobbie, and Keith B. Lucas. "One class, many worlds." International Journal of Science Education 21, no. 1 (January 1999): 59–75. http://dx.doi.org/10.1080/095006999290831.

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10

Désir, Chesner, Simon Bernard, Caroline Petitjean, and Laurent Heutte. "One class random forests." Pattern Recognition 46, no. 12 (December 2013): 3490–506. http://dx.doi.org/10.1016/j.patcog.2013.05.022.

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11

Livi, Lorenzo, Alireza Sadeghian, and Witold Pedrycz. "Entropic One-Class Classifiers." IEEE Transactions on Neural Networks and Learning Systems 26, no. 12 (December 2015): 3187–200. http://dx.doi.org/10.1109/tnnls.2015.2418332.

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12

Younghwan Kim, Younghwan Kim, and Huy Kang Kim Younghwan Kim. "Cluster-based Deep One-Class Classification Model for Anomaly Detection." 網際網路技術學刊 22, no. 4 (July 2021): 903–11. http://dx.doi.org/10.53106/160792642021072204017.

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13

Lee, Gyemin. "Creating Level Set Trees Using One-Class Support Vector Machines." Journal of KIISE 42, no. 1 (January 15, 2015): 86–92. http://dx.doi.org/10.5626/jok.2015.42.1.86.

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14

Carroll, Pamela S. "Metamorphosis: One Teacher's Change/One Class' Reaction." English Journal 83, no. 6 (October 1994): 22. http://dx.doi.org/10.2307/820257.

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15

Carroll, Pamela S. "Metamorphosis: One Teacher’s Change/One Class’ Reaction." English Journal 83, no. 6 (October 1, 1994): 22–28. http://dx.doi.org/10.58680/ej19947541.

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16

Syrovatskyi, V. N. "Functional Models in De Branges Spaces of One Class Commutative Operators." Zurnal matematiceskoj fiziki, analiza, geometrii 10, no. 4 (December 25, 2014): 430–50. http://dx.doi.org/10.15407/mag10.04.430.

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17

Lunyov, A. A., and E. V. Oliynyk. "On Integration of One Class of Systems of Lax-Type Equations." Zurnal matematiceskoj fiziki, analiza, geometrii 11, no. 1 (March 25, 2015): 45–62. http://dx.doi.org/10.15407/mag11.01.045.

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18

G Lachake, Vikram, and Prakash P Rokade. "A Review on OCCT: A One Class Clustering Tree (OCCT) for Implementing One-to-Many Data Linkage." International Journal of Scientific Engineering and Research 3, no. 8 (August 27, 2015): 58–61. https://doi.org/10.70729/ijser15408.

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19

Leung, Denny H., and Wee-Kee Tang. "Functions of Baire class one." Fundamenta Mathematicae 179, no. 3 (2003): 225–47. http://dx.doi.org/10.4064/fm179-3-3.

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20

Chen, Yanyan, Kuaini Wang, and Ping Zhong. "One-Class Support Tensor Machine." Knowledge-Based Systems 96 (March 2016): 14–28. http://dx.doi.org/10.1016/j.knosys.2016.01.007.

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21

Tsylova, E. G. "One class of combinatorial identities." Journal of Soviet Mathematics 39, no. 2 (October 1987): 2672–78. http://dx.doi.org/10.1007/bf01084978.

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22

Villaseñor, Ramón Fuentes, José L. López Bonilla, Gerardo Ovando Zuñiga, and Tonatiuh Matos. "Space-time of class one." General Relativity and Gravitation 21, no. 8 (August 1989): 777–84. http://dx.doi.org/10.1007/bf00758982.

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23

Oza, Poojan, and Vishal M. Patel. "One-Class Convolutional Neural Network." IEEE Signal Processing Letters 26, no. 2 (February 2019): 277–81. http://dx.doi.org/10.1109/lsp.2018.2889273.

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24

Subha, S., and J. G. R. Sathiaseelan. "Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique in IoT Healthcare Data." Indian Journal Of Science And Technology 17, no. 5 (January 31, 2024): 386–96. http://dx.doi.org/10.17485/ijst/v17i5.1645.

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Objectives: This study addresses the concept drift issue in anomaly detection for IoT systems. The objective is to develop a novel approach that effectively handles the dynamic nature of IoT data. Methods: The proposed COMCADSET (Combination of One-Class and Multi-Class Anomaly Detection Using Under-Sampling and Ensemble Technique) addresses the concept drift challenge. It adapts to evolving data distributions, detects anomalies in IoT healthcare data, mitigates class distribution imbalances through under-sampling, and enhances performance with ensemble techniques. The approach involves four phases: multi-class anomaly spotting, one-class anomaly isolation, concept-drift-free dataset creation, and robust anomaly detection using ensembles. Evaluation utilizes the "Heart Failure Prediction" dataset from Kaggle, with comprehensive experiments and three classification algorithms. COMCADSET's innovation merges one-class and multi-class anomaly detection, under-sampling, and ensemble classification. It's compared against gold standards for classification accuracy, concept drift management, and anomaly detection performance. Findings: Conduct comprehensive experiments using a concept drift dataset and three classification algorithms to evaluate the efficacy of the COMCADSET technique. The experimental result shows the proposed COMCADSET technique attains an impressive 98.401% accuracy, decisively enhancing classification accuracy by adeptly addressing concept drift and identifying anomalies in IoT data. Early detection of abnormal behaviour prevents more significant issues and potential security vulnerabilities in IoT systems. Novelty: The novelty of the COMCADSET technique lies in its ability to address the concept drift issue and improve anomaly detection accuracy in IoT systems. By integrating one-class and multi-class anomaly detection, under-sampling, and ensemble techniques, the proposed approach provides a robust solution for handling the dynamic nature of IoT data. Keywords: Anomaly Detection, Concept Drift, Ensemble Classification, Internet of Things, Under­Sampling
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25

Yeh, Chi-Yuan, Zhi-Ying Lee, and Shie-Jue Lee. "BOOSTING ONE-CLASS SUPPORT VECTOR MACHINES FOR MULTI-CLASS CLASSIFICATION." Applied Artificial Intelligence 23, no. 4 (March 31, 2009): 297–315. http://dx.doi.org/10.1080/08839510902787397.

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26

Kang, Seokho, Sungzoon Cho, and Pilsung Kang. "Multi-class classification via heterogeneous ensemble of one-class classifiers." Engineering Applications of Artificial Intelligence 43 (August 2015): 35–43. http://dx.doi.org/10.1016/j.engappai.2015.04.003.

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27

Choinski, Elizabeth, and Michelle Emanuel. "The one‐minute paper and the one‐hour class." Reference Services Review 34, no. 1 (January 2006): 148–55. http://dx.doi.org/10.1108/00907320610648824.

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28

English, Coralie. "Circuit class therapy versus one-to-one therapy sessions." International Journal of Therapy and Rehabilitation 14, no. 5 (May 2007): 202. http://dx.doi.org/10.12968/ijtr.2007.14.5.23536.

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29

Fitzpatrick, J. Michael, and Michael R. Leuze. "A class of one-to-one two-dimensional transformations." Computer Vision, Graphics, and Image Processing 39, no. 3 (September 1987): 369–82. http://dx.doi.org/10.1016/s0734-189x(87)80187-1.

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30

Fitzpatrick, J. Michael, and Michael R. Leuze. "A class of one-to-one two-dimensional transformations." Computer Vision, Graphics, and Image Processing 37, no. 3 (March 1987): 438. http://dx.doi.org/10.1016/0734-189x(87)90049-1.

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31

Sato, Kate, Toshiyuki Sakabe, and Robert Olson. "An investigation into implementing out-of-class English support in one Japanese University." International Journal of Teaching and Education 11, no. 1 (May 2, 2023): 82–103. http://dx.doi.org/10.52950/te.2023.11.1.006.

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The purpose of this study is to assess the potential for an out-of-class EFL programme to be established in a Japanese university in the context of the globalisation of higher educational institutions according to MEXT (Ministry of Education, Culture, Sports, Science and Technology) policies. The methodology centres on a mixed methods approach with the analysis of two parts each with two steps. Part 1 was an external evaluation consisting of (1) interviews and (2) visits to five other comparable universities with established programmes. Part 2 was an internal evaluation consisting of (1) a survey of students’ perceptions of learning English and (2) the feedback from two facilitators who lead a trial out-of-class English programme over a one-year period. Results from the external evaluation indicate that without the support of the institution, faculty-led initiatives will result in small programmes with limited facilities while institution-led initiatives have the potential to create a larger impact within the institution. The internal evaluation revealed a number of hindrances to implementing an out-of-class programme including perceptions of the students’ foreign language learning and how they like to pass their time out of class. Points for the university to consider are recommended. While this investigation focuses on one university at a set time, others in similar situations may find the results of interest to themselves.
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32

Yazdi, Mehdi. "On Thurston’s Euler class-one conjecture." Acta Mathematica 225, no. 2 (2020): 313–68. http://dx.doi.org/10.4310/acta.2020.v225.n2.a3.

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33

Kuzmin, E. V., and D. J. Chalyy. "On one class of counter machines." Automatic Control and Computer Sciences 44, no. 7 (December 2010): 447–51. http://dx.doi.org/10.3103/s0146411610070114.

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34

Demyankov, N. A., and V. S. Klimov. "About One Class of Operators Inclusions." Modeling and Analysis of Information Systems 19, no. 3 (February 27, 2015): 63–72. http://dx.doi.org/10.18255/1818-1015-2012-3-63-72.

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The operator inclusion 0 ∈ A(x)+N(x) is studied. The main results refer to the case, when A – a bounded operator of monotone type from a reflexive space into conjugate to it, N – a conevalued operator. No solution criterion of the viewed inclusion is set up. Integer characteristics of multivalued mappings with homotopy invariance and additivity are introduced. Application to the theory of variational inequalities with multivalued operators is identified.
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35

Shen, Bin, Cheng Lu, Qifan Wang, Si Chen, and Yan Yan. "One-Class Maximum Margin Matrix Factorization." Electronic Imaging 2016, no. 11 (February 14, 2016): 1–6. http://dx.doi.org/10.2352/issn.2470-1173.2016.11.imawm-472.

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36

Satimov, N. Yu, and G. I. Ibragimov. "One class of simultaneous pursuit games." Russian Mathematics 56, no. 5 (April 17, 2012): 39–46. http://dx.doi.org/10.3103/s1066369x12050052.

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37

Karem M., Mohammed, and Ahmed Nori. "Blind Steganalysis using One-Class Classification." AL-Rafidain Journal of Computer Sciences and Mathematics 13, no. 2 (January 3, 2020): 28–41. http://dx.doi.org/10.33899/csmj.2020.163518.

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38

Varcoe, Rae. "Day One Med School Class Reunion." Annals of Internal Medicine 175, no. 4 (April 2022): 615. http://dx.doi.org/10.7326/m21-4146.

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39

TAX, DAVID M. J., and PIOTR JUSZCZAK. "KERNEL WHITENING FOR ONE-CLASS CLASSIFICATION." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (May 2003): 333–47. http://dx.doi.org/10.1142/s021800140300240x.

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In one-class classification one tries to describe a class of target data and to distinguish it from all other possible outlier objects. Obvious applications are areas where outliers are very diverse or very difficult or expensive to measure, such as in machine diagnostics or in medical applications. In order to have a good distinction between the target objects and the outliers, good representation of the data is essential. The performance of many one-class classifiers critically depends on the scaling of the data and is often harmed by data distributions in (nonlinear) subspaces. This paper presents a simple preprocessing method which actively tries to map the data to a spherical symmetric cluster and is almost insensitive to data distributed in subspaces. It uses techniques from Kernel PCA to rescale the data in a kernel feature space to unit variance. This transformed data can now be described very well by the Support Vector Data Description, which basically fits a hypersphere around the data. The paper presents the methods and some preliminary experimental results.
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40

Semenov, A. A., and I. V. Otpuschennikov. "On one class of decision diagrams." Automation and Remote Control 77, no. 4 (April 2016): 617–28. http://dx.doi.org/10.1134/s000511791604007x.

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41

Chen, Yanyan, and Ping Zhong. "Linear One-Class Support Tensor Machine." International Journal of Signal Processing, Image Processing and Pattern Recognition 9, no. 9 (September 30, 2016): 379–88. http://dx.doi.org/10.14257/ijsip.2016.9.9.35.

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42

Giaimo, Michael S. "Ill Will and Class of One." Land Use Law & Zoning Digest 55, no. 2 (February 2003): 3–9. http://dx.doi.org/10.1080/00947598.2003.10394238.

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43

Castillo, Enrique, Diego Peteiro-Barral, Bertha Guijarro Berdiñas, and Oscar Fontenla-Romero. "Distributed One-Class Support Vector Machine." International Journal of Neural Systems 25, no. 07 (August 27, 2015): 1550029. http://dx.doi.org/10.1142/s012906571550029x.

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This paper presents a novel distributed one-class classification approach based on an extension of the ν-SVM method, thus permitting its application to Big Data data sets. In our method we will consider several one-class classifiers, each one determined using a given local data partition on a processor, and the goal is to find a global model. The cornerstone of this method is the novel mathematical formulation that makes the optimization problem separable whilst avoiding some data points considered as outliers in the final solution. This is particularly interesting and important because the decision region generated by the method will be unaffected by the position of the outliers and the form of the data will fit more precisely. Another interesting property is that, although built in parallel, the classifiers exchange data during learning in order to improve their individual specialization. Experimental results using different datasets demonstrate the good performance in accuracy of the decision regions of the proposed method in comparison with other well-known classifiers while saving training time due to its distributed nature.
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44

Kolář, Miroslav. "New class of one-dimensional quasicrystals." Physical Review B 47, no. 9 (March 1, 1993): 5489–92. http://dx.doi.org/10.1103/physrevb.47.5489.

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45

Wils, Patrick. "Neutrino fields of embedding class one." Physica Scripta 41, no. 5 (May 1, 1990): 645. http://dx.doi.org/10.1088/0031-8949/41/5/003.

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46

Gomez-Verdejo, V., J. Arenas-Garcia, M. Lazaro-Gredilla, and Ángel Navia-Vazquez. "Adaptive One-Class Support Vector Machine." IEEE Transactions on Signal Processing 59, no. 6 (June 2011): 2975–81. http://dx.doi.org/10.1109/tsp.2011.2125961.

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47

Kemmler, Michael, Erik Rodner, Esther-Sabrina Wacker, and Joachim Denzler. "One-class classification with Gaussian processes." Pattern Recognition 46, no. 12 (December 2013): 3507–18. http://dx.doi.org/10.1016/j.patcog.2013.06.005.

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48

Dufrenois, F., and J. C. Noyer. "One class proximal support vector machines." Pattern Recognition 52 (April 2016): 96–112. http://dx.doi.org/10.1016/j.patcog.2015.09.036.

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49

Chen, Jixu, and Xiaoming Liu. "Transfer learning with one-class data." Pattern Recognition Letters 37 (February 2014): 32–40. http://dx.doi.org/10.1016/j.patrec.2013.07.017.

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50

Maritz, P. "Almost Baire class one multi-functions." Bulletin of the Australian Mathematical Society 34, no. 2 (October 1986): 297–308. http://dx.doi.org/10.1017/s0004972700010170.

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In this paper we employ quasi-continuous multifunctions and introduce almost Baire class 1 multifunctions in order to generalize a theorem of Kuratowski and also to answer a question posed by him concerning Baire class 1 multifunctions. We also show that certain multifunctions can be decomposed into mutually singular multifunctions.
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