Enriquecimento de Dados com Base em Estatísticas de Grafo de Similaridade para Melhorar o Desempenho em Modelos de ML Supervisionados de Classificação
Resumo
Esta pesquisa propõe um método para o enriquecimento de conjuntos de dados tabulares utilizando estatísticas de grafo, visando melhorar o desempenho de modelos de ML supervisionados de classificação. O método constrói um grafo a partir da similaridade entre as instâncias do conjunto de dados e extrai características do grafo para enriquecer o conjunto de dados original. Avaliado em 10 conjuntos de dados públicos de diferentes áreas do conhecimento, com 7 modelos de aprendizado de máquina, o método proporcionou um aumento médio de 4,9% na acurácia. Os resultados demonstram a efetividade do método como uma alternativa para melhorar o desempenho de modelos em cenários que conjuntos de dados carecem das características necessárias para as abordagens tradicionais de enriquecimento com a utilização de grafo.
Palavras-chave:
Aprendizado de Máquina, Grafo, Similaridade, Grafo por Similaridade, Predição, Enriquecimento de Dados, Características Topológicas Grafo, Redes Complexas
Referências
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Alharbi, A. and Alsubhi, K. (2021). Botnet detection approach using graph-based machine learning. IEEE Access, 9:99166–99180. DOI: 10.1109/ACCESS.2021.3094183.
Ansari, S., Sajjad, F., ul Qayyum, Z., Naveed, N., and Shafi, I. (2013). Diagnosis of vertebral column disorders using machine learning classifiers. In 2013 International Conference on Information Science and Applications, ICISA, pages 1–6.
Barrat, A., Barthélemy, M., Pastor-Satorras, R., and Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11):3747–3752.
Bashir, S., Almazroi, A., Ashfaq, S., Almazroi, A., and Khan, F. (2021). A knowledge-based clinical decision support system utilizing an intelligent ensemble voting scheme for improved cardiovascular disease prediction. IEEE Access, PP:1–1.
Baumann, A., Haupt, J., Gebert, F., and Lessmann, S. (2017). Changing perspectives: Using graph metrics to predict purchase probabilities. Expert Systems with Applications, 94. DOI: 10.1016/j.eswa.2017.10.046.
Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2):163–177.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1):107–117. Proceedings of the Seventh International World Wide Web Conference.
Cardone, B. and Di Martino, F. (2023). A novel classification algorithm based on multi-dimensional f1 fuzzy transform and pca feature extraction. Algorithms, 16:128.
Chang, V., Bailey, J., Xu, Q. A., and Sun, Z. (2022). Pima indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Comput. Appl., 35(22):1–17.
Di, X., Yu, P., Bu, R., and Sun, M. (2020). Mutual information maximization in graph neural networks. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE. DOI: 10.1109/IJCNN48605.2020.9207076.
Dong, Y. and Oyamada, M. (2022). Table enrichment system for machine learning. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’22, pages 3267––3271, New York, NY, USA. Association for Computing Machinery.
Dong, Y., Takeoka, K., Xiao, C., and Oyamada, M. (2020). Efficient joinable table discovery in data lakes: A high-dimensional similarity-based approach. 2021 IEEE 37th International Conference on Data Engineering (ICDE), pages 456–467.
Escovedo, T. and Koshiyama, A. (2020). Introducao a Data Science - Algoritmos de Machine Learning e metodos de analise. Casa do Codigo.
Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40:35–41.
Gan, G., Ma, C., and Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Series on Statistics and Applied Probability. Society for Industrial and Applied Mathematics.
Garate-Escamila, A. K., Hajjam El Hassani, A., and Andres, E. (2020). Classification models for heart disease prediction using feature selection and pca. Informatics in Medicine Unlocked, 19:100330.
Gottschalk, S. and Demidova, E. (2022). Tab2kg: Semantic table interpretation with lightweight semantic profiles. Semantic Web, 13(3):571––597.
Gulum, M. (2018). Horse racing prediction using graph-based features. PhD thesis.
Gupta, M. and Chandrasekaran, V. (2021). A study and analysis of machine learning techniques in predicting wine quality. International Journal of Recent Technology and Engineering, 10:314–321.
Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier Science.
Jalali, V., Leake, D., and Forouzandehmehr, N. (2017). Learning and applying case adaptation rules for classification: An ensemble approach. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17.
Jiomekong, A. and Foko, B. (2022). Towards an approach based on knowledge graph refinement for tabular data to knowledge graph matching. Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab), CEUR-WS. org.
Kibria, H. (2022). An ensemble approach for the prediction of diabetes mellitus using a soft voting classifier with an explainable AI. ” sensors. Sensors, 22.
Kumar, S., Agrawal, K., and Mandan, N. (2020). Red wine quality prediction using machine learning techniques. In 2020 International Conference on Computer Communication and Informatics (ICCCI), pages 1–6.
Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms.
Langville, A. and Meyer, C. (2004). A survey of eigenvector methods of web information retrieval. SIAM Review, 47.
Naveen, Sharma, R. K., and Ramachandran Nair, A. (2019). Efficient breast cancer prediction using ensemble machine learning models. In 2019 4th International Conference on Recent Trends on Electronics, Information, Communication Technology (RTEICT), pages 100–104.
Needham, M. and Hodler, A. (2019). Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O’Reilly Media.
Newman, M. (2018). Networks. Oxford University Press.
Newman, M. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1):39–54.
Ojha, V. and Nicosia, G. (2020). Multi-objective optimisation of multi-output neural trees. 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–8.
Onnela, J.-P., Saramäki, J., Kertész, J., and Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E, 71:065103.
Putatunda, S. (2020). A hybrid deep learning approach for diagnosis of the erythemato-squamous disease. In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pages 1–6.
Raihan-Al-Masud, M. and Mondal, M. R. H. (2020). Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms. PLoS One.
Ramasamy, M., Abdulkadhar, S., and Natarajan, J. (2020). Deep neural network for the automatic classification of vertebral column disorders.
Rathore, A. S., Arjaria, S., Gupta, M., Chaubey, G., Mishra, A., and Rajpoot, V. (2022). Erythemato-squamous diseases prediction and interpretation using explainable ai. IETE Journal of Research.
Rehman, Z., Fayyaz, H., Shah, A., Aslam, N., Hanif, M., and Abbas, S. (2018). Performance evaluation of mlpnn and nb: A comparative study on car evaluation dataset.
Reshi, A. A., Ashraf, I., Rustam, F., Shahzad, H. F., Mehmood, A., and Choi, G. S. (2021). Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms. PeerJ Comput. Sci., 7(e547):e547.
Saboor, A., Usman, M., Ali, S., Samad, A., Abrar, M. F., and Ullah, N. (2022). A method for improving prediction of human heart disease using machine learning algorithms. Mob. Inf. Syst., 2022:1–9.
Saeys, Y., Inza, I., and Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507–2517.
Sanz, I. and Duarte, O. (2019). Graph-based feature enrichment for online intrusion detection in virtual networks. In Anais Estendidos do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 129–136, Porto Alegre, RS, Brasil. SBC.
Sharma, D. K. and Hota, H. S. (2013). Data mining techniques for prediction of different categories of dermatology diseases. Journal of Management Information and Decision Sciences, 16:103.
Srinivasan, S., Hyman, J. D., O’Malley, D., Karra, S., Viswanathan, H. S., and Srinivasan, G. (2020). Chapter three - machine learning techniques for fractured media. In Moseley, B. and Krischer, L., editors, Machine Learning in Geosciences, volume 61 of Advances in Geophysics, pages 109–150. Elsevier.
Sulc, Z. and Řezankova, H. (2014). Evaluation of recent similarity measures for categorical data.
Tan, P.-N., Steinbach, M., Karpatne, A., and Kumar, V. (2019). Introduction to Data Mining (2nd Edition). Pearson Education, 2nd edition.
Uzut, G. and Buyrukoglu, S. (2020). Hyperparameter optimization of data mining algorithms on car evaluation dataset. Euroasia Journal of Mathematics Engineering Natural and Medical Sciences, 7:70–76.
Zaki, N., Mohamed, E., and Habuza, T. (2021). From tabulated data to knowledge graph: A novel way of improving the performance of the classification models in the healthcare data. SSRN Electronic Journal.
Albreiki, B., Habuza, T., and Zaki, N. (2023). Extracting topological features to identify at-risk students using machine learning and graph convolutional network models. Int. J. Educ. Technol. High. Educ., 20(1). DOI: 10.1186/s41239-023-00389-3.
Alfian, G., Syafrudin, M., Fahrurrozi, I., Fitriyani, N. L., Atmaji, F. T. D., Widodo, T., Bahiyah, N., Benes, F., and Rhee, J. (2022). Predicting breast cancer from risk factors using SVM and extra-trees-based feature selection method. Computers, 11(9):136.
Alharbi, A. and Alsubhi, K. (2021). Botnet detection approach using graph-based machine learning. IEEE Access, 9:99166–99180. DOI: 10.1109/ACCESS.2021.3094183.
Ansari, S., Sajjad, F., ul Qayyum, Z., Naveed, N., and Shafi, I. (2013). Diagnosis of vertebral column disorders using machine learning classifiers. In 2013 International Conference on Information Science and Applications, ICISA, pages 1–6.
Barrat, A., Barthélemy, M., Pastor-Satorras, R., and Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences, 101(11):3747–3752.
Bashir, S., Almazroi, A., Ashfaq, S., Almazroi, A., and Khan, F. (2021). A knowledge-based clinical decision support system utilizing an intelligent ensemble voting scheme for improved cardiovascular disease prediction. IEEE Access, PP:1–1.
Baumann, A., Haupt, J., Gebert, F., and Lessmann, S. (2017). Changing perspectives: Using graph metrics to predict purchase probabilities. Expert Systems with Applications, 94. DOI: 10.1016/j.eswa.2017.10.046.
Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25(2):163–177.
Brin, S. and Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1):107–117. Proceedings of the Seventh International World Wide Web Conference.
Cardone, B. and Di Martino, F. (2023). A novel classification algorithm based on multi-dimensional f1 fuzzy transform and pca feature extraction. Algorithms, 16:128.
Chang, V., Bailey, J., Xu, Q. A., and Sun, Z. (2022). Pima indians diabetes mellitus classification based on machine learning (ML) algorithms. Neural Comput. Appl., 35(22):1–17.
Di, X., Yu, P., Bu, R., and Sun, M. (2020). Mutual information maximization in graph neural networks. In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE. DOI: 10.1109/IJCNN48605.2020.9207076.
Dong, Y. and Oyamada, M. (2022). Table enrichment system for machine learning. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’22, pages 3267––3271, New York, NY, USA. Association for Computing Machinery.
Dong, Y., Takeoka, K., Xiao, C., and Oyamada, M. (2020). Efficient joinable table discovery in data lakes: A high-dimensional similarity-based approach. 2021 IEEE 37th International Conference on Data Engineering (ICDE), pages 456–467.
Escovedo, T. and Koshiyama, A. (2020). Introducao a Data Science - Algoritmos de Machine Learning e metodos de analise. Casa do Codigo.
Freeman, L. (1977). A set of measures of centrality based on betweenness. Sociometry, 40:35–41.
Gan, G., Ma, C., and Wu, J. (2007). Data Clustering: Theory, Algorithms, and Applications. ASA-SIAM Series on Statistics and Applied Probability. Society for Industrial and Applied Mathematics.
Garate-Escamila, A. K., Hajjam El Hassani, A., and Andres, E. (2020). Classification models for heart disease prediction using feature selection and pca. Informatics in Medicine Unlocked, 19:100330.
Gottschalk, S. and Demidova, E. (2022). Tab2kg: Semantic table interpretation with lightweight semantic profiles. Semantic Web, 13(3):571––597.
Gulum, M. (2018). Horse racing prediction using graph-based features. PhD thesis.
Gupta, M. and Chandrasekaran, V. (2021). A study and analysis of machine learning techniques in predicting wine quality. International Journal of Recent Technology and Engineering, 10:314–321.
Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Elsevier Science.
Jalali, V., Leake, D., and Forouzandehmehr, N. (2017). Learning and applying case adaptation rules for classification: An ensemble approach. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17.
Jiomekong, A. and Foko, B. (2022). Towards an approach based on knowledge graph refinement for tabular data to knowledge graph matching. Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab), CEUR-WS. org.
Kibria, H. (2022). An ensemble approach for the prediction of diabetes mellitus using a soft voting classifier with an explainable AI. ” sensors. Sensors, 22.
Kumar, S., Agrawal, K., and Mandan, N. (2020). Red wine quality prediction using machine learning techniques. In 2020 International Conference on Computer Communication and Informatics (ICCCI), pages 1–6.
Kuncheva, L. (2004). Combining Pattern Classifiers: Methods and Algorithms.
Langville, A. and Meyer, C. (2004). A survey of eigenvector methods of web information retrieval. SIAM Review, 47.
Naveen, Sharma, R. K., and Ramachandran Nair, A. (2019). Efficient breast cancer prediction using ensemble machine learning models. In 2019 4th International Conference on Recent Trends on Electronics, Information, Communication Technology (RTEICT), pages 100–104.
Needham, M. and Hodler, A. (2019). Graph Algorithms: Practical Examples in Apache Spark and Neo4j. O’Reilly Media.
Newman, M. (2018). Networks. Oxford University Press.
Newman, M. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1):39–54.
Ojha, V. and Nicosia, G. (2020). Multi-objective optimisation of multi-output neural trees. 2020 IEEE Congress on Evolutionary Computation (CEC), pages 1–8.
Onnela, J.-P., Saramäki, J., Kertész, J., and Kaski, K. (2005). Intensity and coherence of motifs in weighted complex networks. Phys. Rev. E, 71:065103.
Putatunda, S. (2020). A hybrid deep learning approach for diagnosis of the erythemato-squamous disease. In 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), pages 1–6.
Raihan-Al-Masud, M. and Mondal, M. R. H. (2020). Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms. PLoS One.
Ramasamy, M., Abdulkadhar, S., and Natarajan, J. (2020). Deep neural network for the automatic classification of vertebral column disorders.
Rathore, A. S., Arjaria, S., Gupta, M., Chaubey, G., Mishra, A., and Rajpoot, V. (2022). Erythemato-squamous diseases prediction and interpretation using explainable ai. IETE Journal of Research.
Rehman, Z., Fayyaz, H., Shah, A., Aslam, N., Hanif, M., and Abbas, S. (2018). Performance evaluation of mlpnn and nb: A comparative study on car evaluation dataset.
Reshi, A. A., Ashraf, I., Rustam, F., Shahzad, H. F., Mehmood, A., and Choi, G. S. (2021). Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms. PeerJ Comput. Sci., 7(e547):e547.
Saboor, A., Usman, M., Ali, S., Samad, A., Abrar, M. F., and Ullah, N. (2022). A method for improving prediction of human heart disease using machine learning algorithms. Mob. Inf. Syst., 2022:1–9.
Saeys, Y., Inza, I., and Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19):2507–2517.
Sanz, I. and Duarte, O. (2019). Graph-based feature enrichment for online intrusion detection in virtual networks. In Anais Estendidos do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 129–136, Porto Alegre, RS, Brasil. SBC.
Sharma, D. K. and Hota, H. S. (2013). Data mining techniques for prediction of different categories of dermatology diseases. Journal of Management Information and Decision Sciences, 16:103.
Srinivasan, S., Hyman, J. D., O’Malley, D., Karra, S., Viswanathan, H. S., and Srinivasan, G. (2020). Chapter three - machine learning techniques for fractured media. In Moseley, B. and Krischer, L., editors, Machine Learning in Geosciences, volume 61 of Advances in Geophysics, pages 109–150. Elsevier.
Sulc, Z. and Řezankova, H. (2014). Evaluation of recent similarity measures for categorical data.
Tan, P.-N., Steinbach, M., Karpatne, A., and Kumar, V. (2019). Introduction to Data Mining (2nd Edition). Pearson Education, 2nd edition.
Uzut, G. and Buyrukoglu, S. (2020). Hyperparameter optimization of data mining algorithms on car evaluation dataset. Euroasia Journal of Mathematics Engineering Natural and Medical Sciences, 7:70–76.
Zaki, N., Mohamed, E., and Habuza, T. (2021). From tabulated data to knowledge graph: A novel way of improving the performance of the classification models in the healthcare data. SSRN Electronic Journal.
Publicado
14/10/2024
Como Citar
BARCHILON, Ney; LOPES, Hélio Côrtes Vieira; KALINOWSKI, Marcos; PEREZ, Jefry Sastre.
Enriquecimento de Dados com Base em Estatísticas de Grafo de Similaridade para Melhorar o Desempenho em Modelos de ML Supervisionados de Classificação. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 220-233.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.240196.
