Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]

In pattern recognition, multiple-instance learning algorithms have gained importance since they avoid that the user must delimit, the images individually in order to recognize the objects. This is an advantage over traditional learning algorithms since these considerably reduce the time required to...

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Autores:
Tipo de recurso:
Fecha de publicación:
2020
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
spa
OAI Identifier:
oai:repository.udem.edu.co:11407/6049
Acceso en línea:
http://hdl.handle.net/11407/6049
Palabra clave:
Information visualization
Multi-instance learning
Representation
Visual Analysis
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License
http://purl.org/coar/access_right/c_16ec
id REPOUDEM2_3ba44354ce7118e6af2bb419601ed18e
oai_identifier_str oai:repository.udem.edu.co:11407/6049
network_acronym_str REPOUDEM2
network_name_str Repositorio UDEM
repository_id_str
dc.title.none.fl_str_mv Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
title Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
spellingShingle Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
Information visualization
Multi-instance learning
Representation
Visual Analysis
title_short Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
title_full Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
title_fullStr Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
title_full_unstemmed Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
title_sort Visualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]
dc.subject.spa.fl_str_mv Information visualization
Multi-instance learning
Representation
Visual Analysis
topic Information visualization
Multi-instance learning
Representation
Visual Analysis
description In pattern recognition, multiple-instance learning algorithms have gained importance since they avoid that the user must delimit, the images individually in order to recognize the objects. This is an advantage over traditional learning algorithms since these considerably reduce the time required to prepare the data set. However, a disadvantage is that the resulting data sets are often complex, making it difficult to visualize them using traditional information visualization techniques. Thus, this work proposes a tool for the visualization and analysis of data sets of the multi-instance learning paradigm. The visualization proposal was evaluated using the expert criteria. In addition, different tests were carried out that show that a correct visualization can help to make decisions about the data set to improve the classification precision. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2021-02-05T14:58:59Z
dc.date.available.none.fl_str_mv 2021-02-05T14:58:59Z
dc.date.none.fl_str_mv 2020
dc.type.eng.fl_str_mv Article
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dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_6501
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dc.type.driver.none.fl_str_mv info:eu-repo/semantics/article
dc.identifier.issn.none.fl_str_mv 16469895
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/11407/6049
dc.identifier.doi.none.fl_str_mv 10.17013/risti.39.84-99
identifier_str_mv 16469895
10.17013/risti.39.84-99
url http://hdl.handle.net/11407/6049
dc.language.iso.none.fl_str_mv spa
language spa
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dc.relation.citationvolume.none.fl_str_mv 2020
dc.relation.citationissue.none.fl_str_mv 39
dc.relation.citationstartpage.none.fl_str_mv 84
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dc.relation.references.none.fl_str_mv Amores, J., Multiple instance classification: Review, taxonomy and comparative study (2013) Artificial Intelligence, 201, pp. 81-105
Andrews, S., Tsochantaridis, I., Hofmann, T., Support vector machines for multiple-instance learning (2003) Advances in Neural Information Processing Systems, pp. 577-584
Ankerst, M., Keim, D., Kriegel, H., “Circle Segments”: A Technique for Visually Exploring Large Multidimensional Data Sets (1996) In Proc. IEEE Visualization ’96, Hot Topic Session, pp. 5-8. , http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-70761
Arancegui, M.N., Laskurain, X.S., Reflexiones sobre la Industria 4.0 desde el caso vasco (2016) EKONOMIAZ. Revista Vasca De Economía, 89 (1), pp. 142-173. , https://ideas.repec.org/a/ekz/ekonoz/2016106.html
Bunescu, R.C., Mooney, R.J., Multiple instance learning for sparse positive bags (2007) Proceedings of the 24Th International Conference on Machine Learning, pp. 105-112
Carbonneau, M.A., Cheplygina, V., Granger, E., Gagnon, G., Multiple instance learning: A survey of problem characteristics and applications (2018) Pattern Recognition, 77, pp. 329-353. , https://doi.org/10.1016/j.patcog.2017.10.009
Chan, W.W.-Y., A survey on multivariate data visualization. Department of Computer Science and Engineering (2006) Hong Kong University of Science and Technology, 8 (6), pp. 1-29
Cheplygina, V., Tax, D.M.J., Characterizing multiple instance datasets (2015) International Workshop on Similarity-Based Pattern Recognition, pp. 15-27
Cleveland, W.S., McGill, R., Graphical perception: Theory, experimentation, and application to the development of graphical methods (1984) Journal of the American Statistical Association, 79 (387), pp. 531-554. , https://doi.org/10.1080/01621459.1984.10478080
Cleveland, W.S., McGill, R., Cleveland, S., The Many Faces of a Scafferplot (2011) Faces, 79 (388), pp. 807-822
Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T., Solving the multiple instance problem with axis-parallel rectangles (1997) Artificial Intelligence, 89 (1-2), pp. 31-71. , https://doi.org/10.1016/s0004-3702(96)00034-3
Foulds, J.R., Frank, E., A review of multi-instance learning assumptions (2010) Knowledge Engineering Review, 25 (1), pp. 1-25
Herrera, F., Ventura, S., Bello, R., Cornelis, C., Zafra, A., Sánchez-Tarragó, D., Vluymans, S., Multiple instance learning: Foundations and algorithms (2016) Multiple Instance Learning: Foundations and Algorithms. Springer International Publishing, , https://doi.org/10.1007/978-3-319-47759-6
Huang, X., Wu, L., Ye, Y., A Review on Dimensionality Reduction Techniques (2019) International Journal of Pattern Recognition and Artificial Intelligence, 33 (10), pp. 975-8887. , https://doi.org/10.1142/S0218001419500174
Hyvärinen, A., Oja, E., Independent component analysis: Algorithms and applications (2000) Neural Networks: The Official Journal of The International Neural Network Society, 13 (4-5), pp. 411-430. , https://doi.org/10.1016/s0893-6080(00)00026-5
Inselberg, A., The plane with parallel coordinates (1985) The Visual Computer, 1 (4), pp. 69-91. , https://doi.org/10.1007/BF01898350
Janvrin, D.J., Raschke, R.L., Dilla, W.N., Making sense of complex data using interactive data visualization (2014) Journal of Accounting Education, 32 (4), pp. 31-48. , https://doi.org/10.1016/j.jaccedu.2014.09.003
Keim, D.A., Kriegel, H.P., Visualization techniques for mining large databases: A comparison (1996) IEEE Transactions on Knowledge and Data Engineering, 8 (6), pp. 923-938. , https://doi.org/10.1109/69.553159
Kobourov, S.G., (2012) Spring Embedders and Force Directed Graph Drawing Algorithms, , http://arxiv.org/abs/1201.3011
Kruskal, J.B., Nonmetric multidimensional scaling: A numerical method (1964) Psychometrika, 29 (2), pp. 115-129. , https://doi.org/10.1007/BF02289694
Liu, S., Cui, W., Wu, Y., Liu, M., A survey on information visualization: Recent advances and challenges (2014) The Visual Computer, 30 (12), pp. 1373-1393. , https://doi.org/10.1007/s00371-013-0892-3
Ma, Y., Xu, J., Wu, X., Wang, F., Chen, W., A visual analytical approach for transfer learning in classification (2017) Information Sciences, 390, pp. 54-69
Maaten, L., Hinton, G., Visualizing data using t-SNE (2008) Journal of Machine Learning Research, 9, pp. 2579-2605. , Nov)
Mera, C., Orozco-Alzate, M., Branch, J., Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning (2014) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8814, pp. 266-273. , https://doi.org/10.1007/978-3-319-11758-4_29, Springer Verlag
Mera, C., Orozco-Alzate, M., Branch, J., Mery, D., Automatic visual inspection: An approach with multi-instance learning (2016) Computers in Industry, 83, pp. 46-54. , https://doi.org/10.1016/j.compind.2016.09.002
Roweis, S.T., Saul, L.K., Nonlinear dimensionality reduction by locally linear embedding (2000) Science, 290 (5500), pp. 2323-2326. , https://doi.org/10.1126/science.290.5500.2323
Schölkopf, B., Smola, A., Müller, K.-R., Kernel principal component analysis (1997) International Conference on Artificial Neural Networks, pp. 583-588
Tenenbaum, J.B., de Silva, V., Langford, J.C., A global geometric framework for nonlinear dimensionality reduction (2000) Science, 290 (5500), pp. 2319-2323. , https://doi.org/10.1126/science.290.5500.2319
Turner, N., (2011) A Guide to Carrying out Usability Reviews, , http://www.uxforthemasses.com/usability-reviews/
Wang, W., Wang, H., Dai, G., Wang, H., Visualization of large hierarchical data by circle packing (2006) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems-Chi ’06, 1, p. 517. , https://doi.org/10.1145/1124772.1124851, ACM Press
Weidmann, N., Frank, E., Pfahringer, B., A two-level learning method for generalized multi-instance problems (2003) European Conference on Machine Learning, pp. 468-479
Yang, W., Gao, Y., Cao, L., TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning (2013) Computer Vision and Image Understanding, 117 (10), pp. 1273-1286. , https://doi.org/10.1016/j.cviu.2012.08.010
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rights_invalid_str_mv http://purl.org/coar/access_right/c_16ec
dc.publisher.none.fl_str_mv Associacao Iberica de Sistemas e Tecnologias de Informacao
dc.publisher.program.spa.fl_str_mv Ingeniería de Telecomunicaciones
dc.publisher.faculty.spa.fl_str_mv Facultad de Ingenierías
publisher.none.fl_str_mv Associacao Iberica de Sistemas e Tecnologias de Informacao
dc.source.none.fl_str_mv RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
institution Universidad de Medellín
repository.name.fl_str_mv Repositorio Institucional Universidad de Medellin
repository.mail.fl_str_mv repositorio@udem.edu.co
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spelling 20202021-02-05T14:58:59Z2021-02-05T14:58:59Z16469895http://hdl.handle.net/11407/604910.17013/risti.39.84-99In pattern recognition, multiple-instance learning algorithms have gained importance since they avoid that the user must delimit, the images individually in order to recognize the objects. This is an advantage over traditional learning algorithms since these considerably reduce the time required to prepare the data set. However, a disadvantage is that the resulting data sets are often complex, making it difficult to visualize them using traditional information visualization techniques. Thus, this work proposes a tool for the visualization and analysis of data sets of the multi-instance learning paradigm. The visualization proposal was evaluated using the expert criteria. In addition, different tests were carried out that show that a correct visualization can help to make decisions about the data set to improve the classification precision. © 2020, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.spaAssociacao Iberica de Sistemas e Tecnologias de InformacaoIngeniería de TelecomunicacionesFacultad de Ingenieríashttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85097000991&doi=10.17013%2fristi.39.84-99&partnerID=40&md5=73f707c3612218f3bdc4b178117a3bab2020398499Amores, J., Multiple instance classification: Review, taxonomy and comparative study (2013) Artificial Intelligence, 201, pp. 81-105Andrews, S., Tsochantaridis, I., Hofmann, T., Support vector machines for multiple-instance learning (2003) Advances in Neural Information Processing Systems, pp. 577-584Ankerst, M., Keim, D., Kriegel, H., “Circle Segments”: A Technique for Visually Exploring Large Multidimensional Data Sets (1996) In Proc. IEEE Visualization ’96, Hot Topic Session, pp. 5-8. , http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-70761Arancegui, M.N., Laskurain, X.S., Reflexiones sobre la Industria 4.0 desde el caso vasco (2016) EKONOMIAZ. Revista Vasca De Economía, 89 (1), pp. 142-173. , https://ideas.repec.org/a/ekz/ekonoz/2016106.htmlBunescu, R.C., Mooney, R.J., Multiple instance learning for sparse positive bags (2007) Proceedings of the 24Th International Conference on Machine Learning, pp. 105-112Carbonneau, M.A., Cheplygina, V., Granger, E., Gagnon, G., Multiple instance learning: A survey of problem characteristics and applications (2018) Pattern Recognition, 77, pp. 329-353. , https://doi.org/10.1016/j.patcog.2017.10.009Chan, W.W.-Y., A survey on multivariate data visualization. Department of Computer Science and Engineering (2006) Hong Kong University of Science and Technology, 8 (6), pp. 1-29Cheplygina, V., Tax, D.M.J., Characterizing multiple instance datasets (2015) International Workshop on Similarity-Based Pattern Recognition, pp. 15-27Cleveland, W.S., McGill, R., Graphical perception: Theory, experimentation, and application to the development of graphical methods (1984) Journal of the American Statistical Association, 79 (387), pp. 531-554. , https://doi.org/10.1080/01621459.1984.10478080Cleveland, W.S., McGill, R., Cleveland, S., The Many Faces of a Scafferplot (2011) Faces, 79 (388), pp. 807-822Dietterich, T.G., Lathrop, R.H., Lozano-Pérez, T., Solving the multiple instance problem with axis-parallel rectangles (1997) Artificial Intelligence, 89 (1-2), pp. 31-71. , https://doi.org/10.1016/s0004-3702(96)00034-3Foulds, J.R., Frank, E., A review of multi-instance learning assumptions (2010) Knowledge Engineering Review, 25 (1), pp. 1-25Herrera, F., Ventura, S., Bello, R., Cornelis, C., Zafra, A., Sánchez-Tarragó, D., Vluymans, S., Multiple instance learning: Foundations and algorithms (2016) Multiple Instance Learning: Foundations and Algorithms. Springer International Publishing, , https://doi.org/10.1007/978-3-319-47759-6Huang, X., Wu, L., Ye, Y., A Review on Dimensionality Reduction Techniques (2019) International Journal of Pattern Recognition and Artificial Intelligence, 33 (10), pp. 975-8887. , https://doi.org/10.1142/S0218001419500174Hyvärinen, A., Oja, E., Independent component analysis: Algorithms and applications (2000) Neural Networks: The Official Journal of The International Neural Network Society, 13 (4-5), pp. 411-430. , https://doi.org/10.1016/s0893-6080(00)00026-5Inselberg, A., The plane with parallel coordinates (1985) The Visual Computer, 1 (4), pp. 69-91. , https://doi.org/10.1007/BF01898350Janvrin, D.J., Raschke, R.L., Dilla, W.N., Making sense of complex data using interactive data visualization (2014) Journal of Accounting Education, 32 (4), pp. 31-48. , https://doi.org/10.1016/j.jaccedu.2014.09.003Keim, D.A., Kriegel, H.P., Visualization techniques for mining large databases: A comparison (1996) IEEE Transactions on Knowledge and Data Engineering, 8 (6), pp. 923-938. , https://doi.org/10.1109/69.553159Kobourov, S.G., (2012) Spring Embedders and Force Directed Graph Drawing Algorithms, , http://arxiv.org/abs/1201.3011Kruskal, J.B., Nonmetric multidimensional scaling: A numerical method (1964) Psychometrika, 29 (2), pp. 115-129. , https://doi.org/10.1007/BF02289694Liu, S., Cui, W., Wu, Y., Liu, M., A survey on information visualization: Recent advances and challenges (2014) The Visual Computer, 30 (12), pp. 1373-1393. , https://doi.org/10.1007/s00371-013-0892-3Ma, Y., Xu, J., Wu, X., Wang, F., Chen, W., A visual analytical approach for transfer learning in classification (2017) Information Sciences, 390, pp. 54-69Maaten, L., Hinton, G., Visualizing data using t-SNE (2008) Journal of Machine Learning Research, 9, pp. 2579-2605. , Nov)Mera, C., Orozco-Alzate, M., Branch, J., Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning (2014) Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8814, pp. 266-273. , https://doi.org/10.1007/978-3-319-11758-4_29, Springer VerlagMera, C., Orozco-Alzate, M., Branch, J., Mery, D., Automatic visual inspection: An approach with multi-instance learning (2016) Computers in Industry, 83, pp. 46-54. , https://doi.org/10.1016/j.compind.2016.09.002Roweis, S.T., Saul, L.K., Nonlinear dimensionality reduction by locally linear embedding (2000) Science, 290 (5500), pp. 2323-2326. , https://doi.org/10.1126/science.290.5500.2323Schölkopf, B., Smola, A., Müller, K.-R., Kernel principal component analysis (1997) International Conference on Artificial Neural Networks, pp. 583-588Tenenbaum, J.B., de Silva, V., Langford, J.C., A global geometric framework for nonlinear dimensionality reduction (2000) Science, 290 (5500), pp. 2319-2323. , https://doi.org/10.1126/science.290.5500.2319Turner, N., (2011) A Guide to Carrying out Usability Reviews, , http://www.uxforthemasses.com/usability-reviews/Wang, W., Wang, H., Dai, G., Wang, H., Visualization of large hierarchical data by circle packing (2006) Proceedings of the SIGCHI Conference on Human Factors in Computing Systems-Chi ’06, 1, p. 517. , https://doi.org/10.1145/1124772.1124851, ACM PressWeidmann, N., Frank, E., Pfahringer, B., A two-level learning method for generalized multi-instance problems (2003) European Conference on Machine Learning, pp. 468-479Yang, W., Gao, Y., Cao, L., TRASMIL: A local anomaly detection framework based on trajectory segmentation and multi-instance learning (2013) Computer Vision and Image Understanding, 117 (10), pp. 1273-1286. , https://doi.org/10.1016/j.cviu.2012.08.010RISTI - Revista Iberica de Sistemas e Tecnologias de InformacaoInformation visualizationMulti-instance learningRepresentationVisual AnalysisVisualization multi-instance data sets [Visualización de conjuntos de datos de múltiples instancias]Articleinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Valencia-Duque, J.E., Universidad de Medellín, Medellín, Antioquia 050031, ColombiaMera, C., Instituto Tecnológico Metropolitano (ITM), Medellín, Antioquia 050013, ColombiaSepúlveda, L.M., Universidad de Medellín, Medellín, Antioquia 050031, Colombiahttp://purl.org/coar/access_right/c_16ecValencia-Duque J.E.Mera C.Sepúlveda L.M.11407/6049oai:repository.udem.edu.co:11407/60492021-02-05 09:58:59.765Repositorio Institucional Universidad de Medellinrepositorio@udem.edu.co

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