AutoCAR: Automation and Reproducibility of Classification Method Tests Based on Association Rules
Abstract
There are different classification methods based on association rules. However, it is often difficult to find implementations and carry out evaluations of such methods. To overcome these problems, we propose AutoCAR, a modular tool to automate the execution and evaluation of classification methods based on association rules. AutoCAR enables anyone to easily add and evaluate new selection methods without modifying the tool’s code.
Keywords:
Association Rules, Classification, Tool
References
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Chicco, D. and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1):1–13.
Guerra-Manzanares, A., Bahsi, H., and Nömm, S. (2021). Kronodroid: Time-based hybrid-featured dataset for effective android malware detection and characterization. Computers & Security, 110:102399.
Jiri, F. and Kliegr, T. (2014). Classification based on associations (cba)-a performance analysis. In CEUR.
Li, H. and Sheu, P. C.-Y. (2021). A scalable association rule learning heuristic for large datasets. Journal of Big Data, 8(1):1–32.
Li, W., Han, J., and Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In Proceedings 2001 IEEE international conference on data mining, pages 369–376.
Liu, B., Hsu, W., Ma, Y., et al. (1998). Integrating classification and association rule mining. In Kdd, volume 98, pages 80–86.
Mattiev, J. and Kavsek, B. (2020). Coverage-based classification using association rule mining. Applied Sciences, 10(20).
Rocha, V. d. S., Kreutz, D., Feitosa, E. L., and Pontes, J. (2022). Avaliação de métodos de classificação baseados em regras de associação para detecção de malwares android. In SBSeg22, pages 1–14. SBC.
Wang, M., Su, X., Liu, F., and Cai, R. (2012). A cancer classification method based on association rules. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, pages 1094–1098.
Yin, X. and Han, J. (2003). CPAR: Classification based on predictive association rules. In Proceedings of the 2003 SIAM international conference on data mining, pages 331–335. SIAM."
Alwidian, J., Hammo, B. H., and Obeid, N. (2018). WCBA: Weighted classification based on association rules algorithm for breast cancer disease. Applied Soft Computing, 62:536–549.
Chicco, D. and Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC genomics, 21(1):1–13.
Guerra-Manzanares, A., Bahsi, H., and Nömm, S. (2021). Kronodroid: Time-based hybrid-featured dataset for effective android malware detection and characterization. Computers & Security, 110:102399.
Jiri, F. and Kliegr, T. (2014). Classification based on associations (cba)-a performance analysis. In CEUR.
Li, H. and Sheu, P. C.-Y. (2021). A scalable association rule learning heuristic for large datasets. Journal of Big Data, 8(1):1–32.
Li, W., Han, J., and Pei, J. (2001). CMAR: Accurate and efficient classification based on multiple class-association rules. In Proceedings 2001 IEEE international conference on data mining, pages 369–376.
Liu, B., Hsu, W., Ma, Y., et al. (1998). Integrating classification and association rule mining. In Kdd, volume 98, pages 80–86.
Mattiev, J. and Kavsek, B. (2020). Coverage-based classification using association rule mining. Applied Sciences, 10(20).
Rocha, V. d. S., Kreutz, D., Feitosa, E. L., and Pontes, J. (2022). Avaliação de métodos de classificação baseados em regras de associação para detecção de malwares android. In SBSeg22, pages 1–14. SBC.
Wang, M., Su, X., Liu, F., and Cai, R. (2012). A cancer classification method based on association rules. In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery, pages 1094–1098.
Yin, X. and Han, J. (2003). CPAR: Classification based on predictive association rules. In Proceedings of the 2003 SIAM international conference on data mining, pages 331–335. SIAM."
Published
2022-09-12
How to Cite
ROCHA, Vanderson da Silva; KREUTZ, Diego; FEITOSA, Eduardo.
AutoCAR: Automation and Reproducibility of Classification Method Tests Based on Association Rules. In: TOOLS - BRAZILIAN SYMPOSIUM ON CYBERSECURITY (SBSEG), 22. , 2022, Santa Maria.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 127-134.
DOI: https://doi.org/10.5753/sbseg_estendido.2022.227036.
