Unbalanced data processing using oversampling: machine Learning

Nowadays, the DL algorithms show good results when used in the solution of different problems which present similar characteristics as the great amount of data and high dimensionality. However, one of the main challenges that currently arises is the classification of high dimensionality databases, w...

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Autores:
amelec, viloria
Pineda Lezama, Omar Bonerge
Mercado Caruso, Nohora Nubia
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7655
Acceso en línea:
https://hdl.handle.net/11323/7655
https://doi.org/10.1016/j.procs.2020.07.018
https://repositorio.cuc.edu.co/
Palabra clave:
Imbalance of classes
Microarray databases
Genetic expression
Deep learning techniques
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_92298e822d3b180f11911f31e1ced54d
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7655
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Unbalanced data processing using oversampling: machine Learning
title Unbalanced data processing using oversampling: machine Learning
spellingShingle Unbalanced data processing using oversampling: machine Learning
Imbalance of classes
Microarray databases
Genetic expression
Deep learning techniques
title_short Unbalanced data processing using oversampling: machine Learning
title_full Unbalanced data processing using oversampling: machine Learning
title_fullStr Unbalanced data processing using oversampling: machine Learning
title_full_unstemmed Unbalanced data processing using oversampling: machine Learning
title_sort Unbalanced data processing using oversampling: machine Learning
dc.creator.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
Mercado Caruso, Nohora Nubia
dc.contributor.author.spa.fl_str_mv amelec, viloria
Pineda Lezama, Omar Bonerge
Mercado Caruso, Nohora Nubia
dc.subject.spa.fl_str_mv Imbalance of classes
Microarray databases
Genetic expression
Deep learning techniques
topic Imbalance of classes
Microarray databases
Genetic expression
Deep learning techniques
description Nowadays, the DL algorithms show good results when used in the solution of different problems which present similar characteristics as the great amount of data and high dimensionality. However, one of the main challenges that currently arises is the classification of high dimensionality databases, with very few samples and high-class imbalance. Biomedical databases of gene expression microarrays present the characteristics mentioned above, presenting problems of class imbalance, with few samples and high dimensionality. The problem of class imbalance arises when the set of samples belonging to one class is much larger than the set of samples of the other class or classes. This problem has been identified as one of the main challenges of the algorithms applied in the context of Big Data. The objective of this research is the study of genetic expression databases, using conventional methods of sub and oversampling for the balance of classes such as RUS, ROS and SMOTE. The databases were modified by applying an increase in their imbalance and in another case generating artificial noise.
publishDate 2020
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dc.date.accessioned.none.fl_str_mv 2021-01-04T21:18:03Z
dc.date.available.none.fl_str_mv 2021-01-04T21:18:03Z
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dc.identifier.issn.spa.fl_str_mv 1877-0509
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.procs.2020.07.018
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identifier_str_mv 1877-0509
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/7655
https://doi.org/10.1016/j.procs.2020.07.018
https://repositorio.cuc.edu.co/
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language eng
dc.relation.references.spa.fl_str_mv [1] Bolón-Canedo, V., Alonso-Betanzos, A., López-de-Ullibarri, I., & Cao, R. (2019). Challenges and Future Trends for Microarray Analysis. In Microarray Bioinformatics (pp. 283-293). Humana, New York, NY.
[2] Sayed, S., Nassef, M., Badr, A., & Farag, I. (2019). A nested genetic algorithm for feature selection in high-dimensional cancer microarray datasets. Expert Systems with Applications, 121, 233-243.
[3] Pal, M.: Extreme learning machine for land cover classification. International Journal of Remote Sensing, 30(14), pp. 3835–3841 (2008)
[4] Guillen, P., & Ebalunode, J. (2016, December). Cancer classification based on microarray gene expression data using deep learning. In 2016 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1403-1405). IEEE.
[5] Nene, S.: Deep learning for natural languaje processing. International Research Journal of Engineering Technology, 4, pp. 930–933 (2017)
[6] Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., ... & Lundin, J. (2018). Deep learning-based tissue analysis predicts outcome in colorectal cancer. Scientific reports, 8(1), 1-11.
[7] Reyes-Nava, A., Sánchez, J. S., Alejo, R., Flores-Fuentes, A. A., & Rendón-Lara, E. (2018, June). Performance analysis of deep neural networks for classification of gene-expression microarrays. In Mexican Conference on Pattern Recognition (pp. 105-115). Springer, Cham.
[8] Viloria, A., & Lezama, O. B. P. (2019). Improvements for determining the number of clusters in k-means for innovation databases in SMEs. In Procedia Computer Science (Vol. 151, pp. 1201–1206). Elsevier B.V. https://doi.org/10.1016/j.procs.2019.04.172.
[9] Flores-Fuentes, A. A., & Granda-Gutiérrez, E. E. (2019, March). Using Deep Learning to Classify Class Imbalanced Gene-Expression Microarrays Datasets. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings (Vol. 11401, p. 46). Springer.
[10] Ding, L., & McDonald, D. J. (2017). Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression. Bioinformatics, 33(14), i350-i358.
[11] Zeebaree, D. Q., Haron, H., & Abdulazeez, A. M. (2018, October). Gene selection and classification of microarray data using convolutional neural network. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (pp. 145-150). IEEE.
[12] Panda, M. (2017). Elephant search optimization combined with deep neural network for microarray data analysis. Journal of King Saud University-Computer and Information Sciences.
[13] Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., ... & Claassen, M. (2018). Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific reports, 8(1), 1-11.
[14] Liu, S., Mocanu, D. C., Matavalam, A. R. R., Pei, Y., & Pechenizkiy, M. (2019). Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware. arXiv preprint arXiv:1901.09181.
[15] Shahane, R., Ismail, M., & Prabhu, C. S. R. (2019). A Survey on Deep Learning Techniques for Prognosis and Diagnosis of Cancer from Microarray Gene Expression Data. Journal of Computational and Theoretical Nanoscience, 16(12), 5078-5088.
[16] Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., ... & Litjens, G. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology.
[17] Salman, H. K.: Cost-Sensitive Learning of Deep Feature Representations from Imbalanced Data. IEEE Transactions on Neural Networks and Learning Systems (2017)
[18] Nguyen, A.B., Phung, S.L.: A supervised learning approach for imbalanced data sets. In: Proc. of the 19th International Conference on Pattern Recognition, pp. 1–4 (2008)
[19] Shekar, B. H., & Dagnew, G. (2020). L1-Regulated Feature Selection and Classification of Microarray Cancer Data Using Deep Learning. In Proceedings of 3rd International Conference on Computer Vision and Image Processing (pp. 227-242). Springer, Singapore.
[20] Basavegowda, H. S., & Dagnew, G. (2020). Deep learning approach for microarray cancer data classification. CAAI Transactions on Intelligence Technology, 5(1), 22-33.
[21] Khaire, U. M., & Dhanalakshmi, R. (2020). High-dimensional microarray dataset classification using an improved adam optimizer (iAdam). Journal of Ambient Intelligence and Humanized Computing, 1-18.
[22] Viloria, A., Varela, N., Lezama, O. B. P., Llinás, N. O., Flores, Y., Palma, H. H., … Marín-González, F. (2020). Classification of Digitized Documents Applying Neural Networks. In Lecture Notes in Electrical Engineering (Vol. 637, pp. 213–220). Springer. https://doi.org/10.1007/978-981-15-2612-1_20
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spelling amelec, viloriaPineda Lezama, Omar BonergeMercado Caruso, Nohora Nubia2021-01-04T21:18:03Z2021-01-04T21:18:03Z20201877-0509https://hdl.handle.net/11323/7655https://doi.org/10.1016/j.procs.2020.07.018Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Nowadays, the DL algorithms show good results when used in the solution of different problems which present similar characteristics as the great amount of data and high dimensionality. However, one of the main challenges that currently arises is the classification of high dimensionality databases, with very few samples and high-class imbalance. Biomedical databases of gene expression microarrays present the characteristics mentioned above, presenting problems of class imbalance, with few samples and high dimensionality. The problem of class imbalance arises when the set of samples belonging to one class is much larger than the set of samples of the other class or classes. This problem has been identified as one of the main challenges of the algorithms applied in the context of Big Data. The objective of this research is the study of genetic expression databases, using conventional methods of sub and oversampling for the balance of classes such as RUS, ROS and SMOTE. The databases were modified by applying an increase in their imbalance and in another case generating artificial noise.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Pineda Lezama, Omar BonergeMercado Caruso, Nohora-will be generated-orcid-0000-0001-9261-8331-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Procedia Computer Sciencehttps://www.sciencedirect.com/science/article/pii/S1877050920316975Imbalance of classesMicroarray databasesGenetic expressionDeep learning techniquesUnbalanced data processing using oversampling: machine LearningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion[1] Bolón-Canedo, V., Alonso-Betanzos, A., López-de-Ullibarri, I., & Cao, R. (2019). Challenges and Future Trends for Microarray Analysis. In Microarray Bioinformatics (pp. 283-293). Humana, New York, NY.[2] Sayed, S., Nassef, M., Badr, A., & Farag, I. (2019). A nested genetic algorithm for feature selection in high-dimensional cancer microarray datasets. Expert Systems with Applications, 121, 233-243.[3] Pal, M.: Extreme learning machine for land cover classification. International Journal of Remote Sensing, 30(14), pp. 3835–3841 (2008)[4] Guillen, P., & Ebalunode, J. (2016, December). Cancer classification based on microarray gene expression data using deep learning. In 2016 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1403-1405). IEEE.[5] Nene, S.: Deep learning for natural languaje processing. International Research Journal of Engineering Technology, 4, pp. 930–933 (2017)[6] Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., ... & Lundin, J. (2018). Deep learning-based tissue analysis predicts outcome in colorectal cancer. Scientific reports, 8(1), 1-11.[7] Reyes-Nava, A., Sánchez, J. S., Alejo, R., Flores-Fuentes, A. A., & Rendón-Lara, E. (2018, June). Performance analysis of deep neural networks for classification of gene-expression microarrays. In Mexican Conference on Pattern Recognition (pp. 105-115). Springer, Cham.[8] Viloria, A., & Lezama, O. B. P. (2019). Improvements for determining the number of clusters in k-means for innovation databases in SMEs. In Procedia Computer Science (Vol. 151, pp. 1201–1206). Elsevier B.V. https://doi.org/10.1016/j.procs.2019.04.172.[9] Flores-Fuentes, A. A., & Granda-Gutiérrez, E. E. (2019, March). Using Deep Learning to Classify Class Imbalanced Gene-Expression Microarrays Datasets. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 23rd Iberoamerican Congress, CIARP 2018, Madrid, Spain, November 19-22, 2018, Proceedings (Vol. 11401, p. 46). Springer.[10] Ding, L., & McDonald, D. J. (2017). Predicting phenotypes from microarrays using amplified, initially marginal, eigenvector regression. Bioinformatics, 33(14), i350-i358.[11] Zeebaree, D. Q., Haron, H., & Abdulazeez, A. M. (2018, October). Gene selection and classification of microarray data using convolutional neural network. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (pp. 145-150). IEEE.[12] Panda, M. (2017). Elephant search optimization combined with deep neural network for microarray data analysis. Journal of King Saud University-Computer and Information Sciences.[13] Arvaniti, E., Fricker, K. S., Moret, M., Rupp, N., Hermanns, T., Fankhauser, C., ... & Claassen, M. (2018). Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Scientific reports, 8(1), 1-11.[14] Liu, S., Mocanu, D. C., Matavalam, A. R. R., Pei, Y., & Pechenizkiy, M. (2019). Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware. arXiv preprint arXiv:1901.09181.[15] Shahane, R., Ismail, M., & Prabhu, C. S. R. (2019). A Survey on Deep Learning Techniques for Prognosis and Diagnosis of Cancer from Microarray Gene Expression Data. Journal of Computational and Theoretical Nanoscience, 16(12), 5078-5088.[16] Bulten, W., Pinckaers, H., van Boven, H., Vink, R., de Bel, T., van Ginneken, B., ... & Litjens, G. (2020). Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. The Lancet Oncology.[17] Salman, H. K.: Cost-Sensitive Learning of Deep Feature Representations from Imbalanced Data. IEEE Transactions on Neural Networks and Learning Systems (2017)[18] Nguyen, A.B., Phung, S.L.: A supervised learning approach for imbalanced data sets. In: Proc. of the 19th International Conference on Pattern Recognition, pp. 1–4 (2008)[19] Shekar, B. H., & Dagnew, G. (2020). L1-Regulated Feature Selection and Classification of Microarray Cancer Data Using Deep Learning. In Proceedings of 3rd International Conference on Computer Vision and Image Processing (pp. 227-242). Springer, Singapore.[20] Basavegowda, H. S., & Dagnew, G. (2020). Deep learning approach for microarray cancer data classification. CAAI Transactions on Intelligence Technology, 5(1), 22-33.[21] Khaire, U. M., & Dhanalakshmi, R. (2020). High-dimensional microarray dataset classification using an improved adam optimizer (iAdam). Journal of Ambient Intelligence and Humanized Computing, 1-18.[22] Viloria, A., Varela, N., Lezama, O. B. P., Llinás, N. O., Flores, Y., Palma, H. H., … Marín-González, F. (2020). Classification of Digitized Documents Applying Neural Networks. In Lecture Notes in Electrical Engineering (Vol. 637, pp. 213–220). 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