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Classification is one of the key issues in the field of data mining and knowledge discovery. This paper implements a method of constructing a fuzzy rule mining classifier, which is extended in the context of classification. There are three stages of this approach: fuzzy rule set extraction, second; a linguistic labeling process that assigns a linguistic label to each fuzzy set. Owing to many attributes in the database, the feature selection process is also carried out, reducing the complexity to build the final classifier. Third: incorporate strategies to avoid rule redundancy and conflict into process mining. We applied the application Multiobjective Evolutionary Fuzzy Classifier (MOFC), which produced a classifier with satisfactory classification accuracy compared to other classifiers such as C4.5. In addition, in terms of classification based on association rules, MOFC can filter the large of rules and be proven to be able to build compact fuzzy models while maintaining a very good level of accuracy and producing a much smaller set of rules. We examine the performance of fuzzy rule classifiers through computational experiments on three benchmark data sets in the UCI machine learning repository.
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A. Fernández, V. López, M. J. del Jesus, and F. Herrera, “Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges,” Knowledge-Based Systems, vol. 80, 2015, doi: 10.1016/j.knosys.2015.01.013.
S. Birtane and H. Korkmaz, “Rule-based fuzzy classifier for spinal deformities,” in Bio-Medical Materials and Engineering, 2014, vol. 24, no. 6. doi: 10.3233/BME-141154.
F. R. Hariri, “Klasifikasi Jenis Golongan Darah Menggunakan Fuzzy C-Means Clustering (FCM) dan Learning Vector Quantization (LVQ),” MATICS, vol. 10, no. 1, 2018, doi: 10.18860/mat. v10i1.5356.
O. Cordón, “A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems,” International Journal of Approximate Reasoning, vol. 52, no. 6. 2011. doi: 10.1016/j.ijar.2011.03.004.
H. Ishibuchi, Y. Kaisho, and Y. Nojma, “Design of linguistically interpretable fuzzy rule-based classifiers: A short review and open questions,” Journal of Multiple-Valued Logic and Soft Computing, vol. 17, no. 2–3, 2011.
S. Elhag, A. Fernández, A. Altalhi, S. Alshomrani, and F. Herrera, “A multi-objective evolutionary fuzzy system to obtain a broad and accurate set of solutions in intrusion detection systems,” Soft Computing, vol. 23, no. 4, 2019, doi: 10.1007/s00500-017-2856-4.
M. Galende-Hernández, G. I. Sainz-Palmero, and M. J. Fuente-Aparicio, “Complexity reduction and interpretability improvement for fuzzy rule systems based on simple interpretability measures and indices by bi-objective evolutionary rule selection,” Soft Computing, vol. 16, no. 3, 2012, doi: 10.1007/s00500-011-0748-6.
J. Novaković, P. Strbac, and D. Bulatović, “Toward optimal feature selection using ranking methods and classification algorithms,” Yugoslav Journal of Operations Research, vol. 21, no. 1, 2011, doi: 10.2298/YJOR1101119N.
J. R. Quinlan, C4. 5: programs for machine learning. Elsevier, 2014.