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Journal articles on the topic 'Classification methods'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 February 2022

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1

Kubade, Harshad M. "The Overview of Bayes Classification Methods." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2801–2. http://dx.doi.org/10.31142/ijtsrd15750.

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2

Crouch, R. J., and R. J. Blong. "Gully sidewall classification: methods and applications." Zeitschrift für Geomorphologie 33, no. 3 (October 5, 1989): 291–305. http://dx.doi.org/10.1127/zfg/33/1989/291.

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3

Ghimire, Santosh. "On the Image Pixels Classification Methods." Journal of the Institute of Engineering 15, no. 2 (July 31, 2019): 202–9. http://dx.doi.org/10.3126/jie.v15i2.27667.

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In this article, we first discuss about the images and image pixels classifications. Then we briefly discuss the importance of classification of images and finally focus on various methods of classification which can be implemented to classify image pixels.

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4

Rasheed, Nishana, and Shreeja R. "Image Classification Methods." International Journal of Engineering Trends and Technology 8, no. 8 (February 25, 2014): 461–64. http://dx.doi.org/10.14445/22315381/ijett-v8p279.

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5

Pekhtasheva, Elena, Anatoly Neverov, Stefan Kubica, and Gennady Zaikov. "Classification of Biodamages, Evaluation and Protection Methods." Chemistry & Chemical Technology 6, no. 4 (December 20, 2012): 459–72. http://dx.doi.org/10.23939/chcht06.04.459.

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6

Othman, Osama M., and Christopher H. Bryant. "Pruning Classification Rules with Instance Reduction Methods." International Journal of Machine Learning and Computing 5, no. 3 (June 2015): 187–91. http://dx.doi.org/10.7763/ijmlc.2015.v5.505.

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7

Babaeva, Vasila. "Error Classification And Methods Of Their Correction." American Journal of Social Science and Education Innovations 02, no. 08 (August 29, 2020): 474–77. http://dx.doi.org/10.37547/tajssei/volume02issue08-76.

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8

Perepelitsa, V. A., I. V. Kozin, and S. V. Kurapov. "Methods of classification and algorithms of graph coloring." Researches in Mathematics 16 (February 7, 2021): 135. http://dx.doi.org/10.15421/240816.

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We study the connection between classifications on finite set and the problem of graph coloring. We consider the optimality criterion for classification of special type: h-classifications, which are built on the base of proximity measure. It is shown that the problem of finding the optimal h-classification can be reduced to the problem of coloring of non-adjacency graph vertices by the smallest possible number of colors. We consider algorithms of proper coloring of graph vertices.

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9

Marini, Federico. "Classification Methods in Chemometrics." Current Analytical Chemistry 6, no. 1 (January 1, 2010): 72–79. http://dx.doi.org/10.2174/157341110790069592.

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10

WATADA, Junzo. "Methods for Fuzzy Classification." Journal of Japan Society for Fuzzy Theory and Systems 4, no. 1 (1992): 61–73. http://dx.doi.org/10.3156/jfuzzy.4.1_61.

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11

Benediktsson, J. A., and P. H. Swain. "Consensus theoretic classification methods." IEEE Transactions on Systems, Man, and Cybernetics 22, no. 4 (1992): 688–704. http://dx.doi.org/10.1109/21.156582.

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12

Ekin, Oya, Peter L. Hammer, Alexander Kogan, and Pawel Winter. "Distance-Based Classification Methods." INFOR: Information Systems and Operational Research 37, no. 3 (August 1999): 337–52. http://dx.doi.org/10.1080/03155986.1999.11732388.

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13

Bischl, Bernd, Julia Schiffner, and Claus Weihs. "Benchmarking local classification methods." Computational Statistics 28, no. 6 (May 8, 2013): 2599–619. http://dx.doi.org/10.1007/s00180-013-0420-y.

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14

Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprsarchives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

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15

Han, Seokwan, and Jinsoo Hwang. "Network Classification of P2P Traffic with Various Classification Methods." Korean Journal of Applied Statistics 28, no. 1 (February 28, 2015): 1–8. http://dx.doi.org/10.5351/kjas.2015.28.1.001.

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16

Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprs-archives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recent researches and most of researches work with the gridded LiDAR point cloud. In grid based data processing of the LiDAR data, the characteristic point loss in the LiDAR point cloud especially vegetation and buildings or losing height accuracy during the interpolation stage are inevitable. In this case, the possible solution is the use of the raw point cloud data for classification to avoid data and accuracy loss in gridding process. In this study, the point based classification possibilities of the LiDAR point cloud is investigated to obtain more accurate classes. The automatic point based approaches, which are based on hierarchical rules, have been proposed to achieve ground, building and vegetation classes using the raw LiDAR point cloud data. In proposed approaches, every single LiDAR point is analyzed according to their features such as height, multi-return, etc. then automatically assigned to the class which they belong to. The use of un-gridded point cloud in proposed point based classification process helped the determination of more realistic rule sets. The detailed parameter analyses have been performed to obtain the most appropriate parameters in the rule sets to achieve accurate classes. The hierarchical rule sets were created for proposed Approach 1 (using selected spatial-based and echo-based features) and Approach 2 (using only selected spatial-based features) and have been tested in the study area in Zekeriyaköy, Istanbul which includes the partly open areas, forest areas and many types of the buildings. The data set used in this research obtained from Istanbul Metropolitan Municipality which was collected with ‘Riegl LSM-Q680i’ full-waveform laser scanner with the density of 16 points/m2. The proposed automatic point based Approach 1 and Approach 2 classifications successfully produced the ground, building and vegetation classes which were very similar although different features were used.

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17

Mohammed, Somia B., Ahmed Khalid, and SaifeEldin F. Osman. "A Survey of Classification Methods." International Journal of Advanced Engineering Research and Science 3, no. 10 (2016): 148–52. http://dx.doi.org/10.22161/ijaers/310.24.

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18

Hu, Ruo, and Zan Fu Xie. "Classification of Knowledge Discovery Methods." Applied Mechanics and Materials 63-64 (June 2011): 859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.859.

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Knowledge Discovery, the science and technology of exploring knowledge in order to discover previously unknown patterns, is a part of the overall process of getting information in databases. In today’s computer-driven world, these databases contain a lot of information. The significant value of this information makes knowledge discovery a matter of considerable importance and necessity. A decision tree is a predictive model which can be used to represent both classifiers and regression models. When a decision tree is used for classification tasks, it is more appropriately referred to as a classification tree.in this paper, Classification Trees Method of Knowledge Discovery In Internet is given.

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19

Moskvin, Leonid N. "A classification of separation methods." Vestnik of Saint Petersburg University. Physics. Chemistry 4(62), no. 2 (2017): 163–214. http://dx.doi.org/10.21638/11701/spbu04.2017.206.

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20

Petricas, A. Zh, D. V. Medvedev, and E. B. Olkhovskaya. "Classification of local anesthesia methods." Stomatologiya 95, no. 4 (2016): 4. http://dx.doi.org/10.17116/stomat20169544-9.

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21

Gubarev, V. V., A. A. Gorshenkov, Yu N. Klikushin, and V. Yu Kobenko. "Classification measurements: Methods and implementation." Optoelectronics, Instrumentation and Data Processing 49, no. 2 (March 2013): 171–77. http://dx.doi.org/10.3103/s875669901302009x.

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22

FUKAGATA, Koji. "Classification of Flow Control Methods." Journal of the Society of Mechanical Engineers 115, no. 1127 (2012): 686–87. http://dx.doi.org/10.1299/jsmemag.115.1127_686.

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23

Moskvin, Leonid N. "A Classification of Separation Methods." Separation & Purification Reviews 45, no. 1 (February 28, 2014): 1–27. http://dx.doi.org/10.1080/15422119.2014.897235.

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24

Ştefan, Raluca-Mariana, Măriuţa Şerban, Iulian-Ion Hurloiu, and Bianca-Florentina Rusu. "Kernel Methods for Data Classification." International conference KNOWLEDGE-BASED ORGANIZATION 22, no. 3 (June 1, 2016): 572–75. http://dx.doi.org/10.1515/kbo-2016-0098.

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Abstract In the past decades, the exponential evolution of data collection for macroeconomic databases in digital format caused a huge increase in their volume. As a consequence, the automatic organization and the classification of macroeconomic data show a significant practical value. Various techniques for categorizing data are used to classify numerous macroeconomic data according to the classes they belong to. Since the manual construction of some of the classifiers is difficult and time consuming, are preferred classifiers that learn from action examples, a process which forms the supervised classification type. A variant of solving the problem of data classification is the one of using the kernel type methods. These methods represent a class of algorithms used in the automatic analysis and classification of information. Most algorithms of this section focus on solving convex optimization problems and calculating their own values. They are efficient in terms of computation time and are very stable statistically. Shaw-Taylor, J. and Cristianini, N. have demonstrated that this type of approach to data classification is robust and efficient in terms of detection of existing stable patterns in a finite array of data. Thus, in a modular manner data will be incorporated into a space where it can cause certain linear relationship.

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25

Khokar, N. "A Classification of Shedding Methods." Journal of the Textile Institute 90, no. 4 (January 1999): 570–79. http://dx.doi.org/10.1080/00405000.1999.10750054.

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26

H T, Bhavana, and Srikanth H T. "Pattern Classification Methods: A Survey." International Journal of Scientific and Research Publications (IJSRP) 9, no. 8 (August 12, 2019): p9299. http://dx.doi.org/10.29322/ijsrp.9.08.2019.p9299.

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27

Bourel, M., and A. M. Segura. "Multiclass classification methods in ecology." Ecological Indicators 85 (February 2018): 1012–21. http://dx.doi.org/10.1016/j.ecolind.2017.11.031.

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28

Hartmann, Jochen, Juliana Huppertz, Christina Schamp, and Mark Heitmann. "Comparing automated text classification methods." International Journal of Research in Marketing 36, no. 1 (March 2019): 20–38. http://dx.doi.org/10.1016/j.ijresmar.2018.09.009.

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29

Barlow, Roger. "Event classification using weighting methods." Journal of Computational Physics 72, no. 1 (September 1987): 202–19. http://dx.doi.org/10.1016/0021-9991(87)90078-7.

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30

Carvalho, Jhonnata Bezerra de, and Getúlio José Amorim do Amaral. "Classification methods for planar shapes." Expert Systems with Applications 151 (August 2020): 113320. http://dx.doi.org/10.1016/j.eswa.2020.113320.

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31

Vrbanec, Tedo, and Ana Meštrović. "Taxonomy of academic plagiarism methods." Zbornik Veleučilišta u Rijeci 9, no. 1 (2021): 283–300. http://dx.doi.org/10.31784/zvr.9.1.17.

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The article gives an overview of the plagiarism domain, with focus on academic plagiarism. The article defines plagiarism, explains the origin of the term, as well as plagiarism related terms. It identifies the extent of the plagiarism domain and then focuses on the plagiarism subdomain of text documents, for which it gives an overview of current classifications and taxonomies and then proposes a more comprehensive classification according to several criteria: their origin and purpose, technical implementation, consequence, complexity of detection and according to the number of linguistic sources. The article suggests the new classification of academic plagiarism, describes sorts and methods of plagiarism, types and categories, approaches and phases of plagiarism detection, the classification of methods and algorithms for plagiarism detection. The title of the article explicitly targets the academic community, but it is sufficiently general and interdisciplinary, so it can be useful for many other professionals like software developers, linguists and librarians.

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32

Bremer, Hanna. "Integrative work methods: Spatial and temporal classification in the tropics." Zeitschrift für Geomorphologie, Supplementary Issues 54, no. 1 (May 1, 2010): 201–25. http://dx.doi.org/10.1127/zfg_suppl/54/2010/201.

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33

Cerri, Ricardo, André Carlos P. L. F. de Carvalho, and Alex A. Freitas. "Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification." Intelligent Data Analysis 15, no. 6 (November 8, 2011): 861–87. http://dx.doi.org/10.3233/ida-2011-0500.

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34

Pushpa, M., and S. Karpagavalli. "Multi-label Classification: Problem Transformation methods in Tamil Phoneme classification." Procedia Computer Science 115 (2017): 572–79. http://dx.doi.org/10.1016/j.procs.2017.09.116.

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35

Xie, Shengkun, and Sridhar Krishnan. "Signal classification via multi-scale PCA and empirical classification methods." International Journal of Mechatronics and Automation 1, no. 3/4 (2011): 213. http://dx.doi.org/10.1504/ijma.2011.045253.

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36

D.E., Narbekov. "On Some Methods Of Economic Risk Management: Essence And Classifications." American Journal of Management and Economics Innovations 3, no. 06 (June 30, 2021): 146–56. http://dx.doi.org/10.37547/tajmei/volume03issue06-22.

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The article notes that economic risk is one of the most multifaceted phenomena, the nature and components of which are not subject to a simple and unambiguous interpretation. But it should be noted that the classification of risks of this type, taking into account the many factors containing its signs is a rather voluminous problem due to the lack of a generally accepted systemic classification. The author argues that the above classification of methods for managing economic risks makes it possible to determine the characteristics used in their classification in the management process as a whole.

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37

SEMENIUK, Oksana. "Classification of tactical methods of interview." Economics. Finances. Law, no. 3(1) (March 31, 2020): 6–10. http://dx.doi.org/10.37634/efp.2020.3(1).1.

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The article describes the concept of interrogation as an important procedural and often practiced investigative action, which is a form of obtaining evidentiary information, a procedural means of generating and verifying evidence. As a result of the interrogation, a considerable part of the information about the event of the crime is obtained and verified, the motives and purpose of the crime, as well as the conditions under which it occurred and which contributed to its commission, are established. Classifications of tactical methods of interrogation are analyzed. The purpose of the article is to research and substantiate the classification of tactical techniques of interrogation. In order to conduct the interrogation effectively, the investigator must be well versed in the psychology of the interviewee, be able to establish correct relationships with them, vary methods of psychological influence or tactical techniques depending on the particular investigative situation, the personality of the interviewee, the evidence available, and so on. By the quality of the information received, the interrogation is divided into the interrogation of the person giving the knowingly false testimony and the interrogation of the person reporting the true information. A person's testimony may be classified as true, false, or false if the interviewee, for various reasons, admits different inaccuracies, distortions, and the like. Depending on the complexity of the investigative situation, the interrogation in the conflict situation differs and the interrogation in the conflict situation. The general procedural task of the interrogation is to obtain from each interviewee all known to him reliable information about the facts and circumstances in which the incident occurred, and about the persons involved in it.

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38

Sokurenko, Ihor. "Classification of management decision making methods." Scientific Bulletin of the Odessa National Economic University 6, no. 269 (2019): 161–77. http://dx.doi.org/10.32680/2409-9260-2019-6-269-161-177.

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39

Gorskiy, M. A., M. S. Kozlova, and D. I. Pugacheva. "MORTGAGE LOAN RISKS: CLASSIFICATION, ASSESSMENT METHODS." Вестник Алтайской академии экономики и права 1, no. 8 2020 (2020): 25–39. http://dx.doi.org/10.17513/vaael.1252.

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40

Clopper, Cynthia G. "Auditory free classification: Methods and analysis." Behavior Research Methods 40, no. 2 (May 2008): 575–81. http://dx.doi.org/10.3758/brm.40.2.575.

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41

Meenakshi, Meenakshi, and Geetika Geetika. "Survey on Classification Methods using WEKA." International Journal of Computer Applications 86, no. 18 (January 16, 2014): 16–19. http://dx.doi.org/10.5120/15085-3330.

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42

Serhiyenko, A. A., and V. A. Serhiyenko. "Diabetic cardiomyopathy: classification, instrumental diagnostic methods." INTERNATIONAL JOURNAL OF ENDOCRINOLOGY (Ukraine) 16, no. 7 (October 1, 2020): 577–87. http://dx.doi.org/10.22141/2224-0721.16.7.2020.219012.

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43

Buchatskaya, Viktoria. "Forecasting Methods Classification and its Applicability." Indian Journal of Science and Technology 8, no. 1 (January 20, 2015): 1–8. http://dx.doi.org/10.17485/ijst/2015/v8i30/84224.

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44

Suvarna, Malini, Siva kumar, and Niranjan U C. "Classification Methods of Skin Burn Images." International Journal of Computer Science and Information Technology 5, no. 1 (February 28, 2013): 109–18. http://dx.doi.org/10.5121/ijcsit.2013.5109.

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45

Bagawade, Mr Ramdas Pandurang. "Comparative Study of Object Classification Methods." International Journal for Research in Applied Science and Engineering Technology 6, no. 3 (March 31, 2018): 1498–505. http://dx.doi.org/10.22214/ijraset.2018.3232.

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46

Honary, Mahsa. "Practical Classification Methods for Indoor Positioning." Open Transportation Journal 6, no. 1 (July 25, 2012): 31–38. http://dx.doi.org/10.2174/1874447801206010031.

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47

Kozlovtseva, G. A., and O. A. Kovalchuk. "Classification of methods of music education." Trends in the development of science and education 60, no. 3 (April 30, 2020): 39–42. http://dx.doi.org/10.18411/lj-04-2020-48.

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48

Larichev, Oleg, Artyom Asanov, and Yevgeny Naryzhny. "Effectiveness evaluation of expert classification methods." European Journal of Operational Research 138, no. 2 (April 2002): 260–73. http://dx.doi.org/10.1016/s0377-2217(01)00245-4.

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49

Kiang, Melody Y. "A comparative assessment of classification methods." Decision Support Systems 35, no. 4 (July 2003): 441–54. http://dx.doi.org/10.1016/s0167-9236(02)00110-0.

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50

van der Voet, Hilko, Pierre M. J. Coenegracht, and Jan B. Hemel. "The evaluation of probabilistic classification methods." Analytica Chimica Acta 209 (1988): 1–27. http://dx.doi.org/10.1016/s0003-2670(00)84546-8.

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