×

EARC: evidential association rule-based classification. (English) Zbl 1475.62186

Summary: As an extension of classical fuzzy rule-based classification, the belief rule-based classification is a promising technique for handling hybrid information with multiple uncertainties in real-world applications. However, the antecedent structure of each resultant rule is fixed and hence may cause overfitting in small instance cases, while some resultant rules are also redundant due to the similarity of neighboring rules. Here, an evidential association rule-based classification method, called EARC, is developed by integrating evidential association rule mining and classification to obtain an accurate and compact classification model. First, new measures of evidential support and confidence are proposed to represent rule interestingness. Then, a three-stage rule mining algorithm is developed to generate a set of evidential classification association rules, including Apriori-based frequent fuzzy itemsets searching for discovering all possible antecedents, evidential consequents deriving in the belief function framework, and reliable rule extracting with measures of evidential support and confidence. Further, to make the classification efficient, the procedures of rule prescreening and rule selection are presented for deleting redundant rules and obtaining an accurate classifier, respectively. At last, an improved belief reasoning process is presented for classifying each input instance by combining the top \(K\) activated rules. Experimental results based on real-world datasets demonstrate the superiority of the proposed method on classification accuracy and interpretability.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)

Software:

CMAR; UCI-ml; JStatCom; KEEL
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Aggarwal, C. C., Data Classification: Algorithms and Applications (2014), Chapman & Hall/ CRC: Chapman & Hall/ CRC Boca Raton, FL · Zbl 1297.68009
[2] Aggarwal, C. C., Data Mining: the Textbook (2015), Springer: Springer New York, NY · Zbl 1311.68001
[3] Agrawal, R.; Srikant, R., Fast algorithms for mining association rules, (Proceedings of the 20th International Conference on Very Large Data Bases (1994)), 487-499
[4] Ajlouni, M. I.A.; Hadi, W.; Alwedyan, J., Detecting phishing websites using associative classification, Eur. J. Business Manage., 5, 36-40 (2013)
[5] Alashkar, T.; Jiang, S.; Fu, Y., Rule-based facial makeup recommendation system, in, (Procedings of IEEE 12th International Conference on Automatic Face & Gesture Recognition (2017)), 325-330
[6] Alcalá-Fdez, J.; Alcalá, R.; Herrera, F., A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning, IEEE Trans. Fuzzy Syst., 19, 857-872 (2011)
[7] Alcalá-Fdez, J.; Fernández, A.; Luengo, J.; Derrac, J.; García, S.; Sánchez, L.; Herrera, F., Keel data-mining software tool: Data set repository, integration of algorithms and experimental analysis framework, J. Multiple-Valued Logic Soft Comput., 17, 255-287 (2011)
[8] Antonelli, M.; Bernardo, D.; Hagras, H.; Marcelloni, F., Multiobjective evolutionary optimization of type-2 fuzzy rule-based systems for financial data classification, IEEE Trans. Fuzzy Syst., 25, 249-264 (2017)
[9] Antonelli, M.; Ducange, P.; Marcelloni, F.; Segatori, A., A novel associative classification model based on a fuzzy frequent pattern mining algorithm, Expert Syst. Appl., 42, 2086-2097 (2015)
[10] Azmi, M.; Runger, G. C.; Liu, G.; Berrado, A., Interpretable regularized class association rules algorithm for classification in a categorical data space, Inf. Sci., 483, 313-331 (2019) · Zbl 1453.62538
[11] Breiman, L.; Friedman, J.; Stone, C. J.; Olshen, R. A., Classification and Regression Trees (1984), Chapman & Hall/ CRC: Chapman & Hall/ CRC Boca Raton, FL · Zbl 0541.62042
[12] Chang, L.; Dong, W.; Yang, J.; Sun, X.; Xu, X.; Xu, X.; Zhang, L., Hybrid belief rule base for regional railway safety assessment with data and knowledge under uncertainty, Inf. Sci., 518, 376-395 (2020)
[13] Chang, L.; Zhou, Z. J.; You, Y.; Yang, L.; Zhou, Z., Belief rule based expert system for classification problems with new rule activation and weight calculation procedures, Inf. Sci., 336, 75-91 (2016)
[14] T. Chen, C. Shang, J. Yang, F. Li, Q. Shen, A new approach for transformation-based fuzzy rule interpolation, IEEE Trans. Fuzzy Syst. (2020) in press.https://doi.org/10.1109/TFUZZ.2019.2949767.
[15] Chen, Z.; Chen, G., Building an associative classifier based on fuzzy association rules, Int. J. Comput. Intell. Syst., 1, 262-273 (2008)
[16] Chi, Z.; Yan, H.; Pham, T., Fuzzy Algorithms with Applications to Image Processing and Pattern Recognition (1996), World Scientific: World Scientific Singapore · Zbl 0942.68001
[17] Dempster, A., Upper and lower probabilities induced by a multi-valued mapping, Ann. Math. Stat., 38, 325-339 (1967) · Zbl 0168.17501
[18] Deng, Z.; Cao, L.; Jiang, Y.; Wang, S., Minimax probability TSK fuzzy system classifier: a more transparent and highly interpretable classification model, IEEE Trans. Fuzzy Syst., 23, 813-826 (2015)
[19] Denoeux, T., A neural network classifier based on Dempster-Shafer theory, IEEE Trans. Syst. Man Cybern. A, 30, 131-150 (2000)
[20] Dešmar, J., Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res., 7, 1-30 (2006) · Zbl 1222.68184
[21] D. Dua, E. Karra Taniskidou, UCI Machine Learning Repository [ http://archive.ics.uci.edu/ml], 2019.
[22] Fang, O. H.; Mustapha, N.; Hamdan, H.; Rosli, R.; Mustapha, A., Informative top-k class associative rule for cancer biomarker discovery on microarray data, Expert Syst. Appl., 146, Article 113169 pp. (2020)
[23] Gacto, M.; Alcalá, R.; Herrera, F., Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures, Inf. Sci., 181, 4340-4360 (2011)
[24] Hadi, W.; Al-Radaidehb, Q. A.; Alhawari, S., Integrating associative rule-based classification with naïve bayes for text classification, Appl. Soft Comput., 69, 344-356 (2018)
[25] Hu, Y.; Chen, R.; Tzeng, G., Finding fuzzy classification rules using data mining techniques, Pattern Recogn. Lett., 24, 509-519 (2003) · Zbl 1053.68084
[26] H. Ishibuchi, T. Nakashima, T. Yamamoto, Fuzzy association rules for handling continuous attributes, in: Proceedings of IEEE International Symposium on Industrial Electronics, 2001, pp. 118-121.
[27] Ishibuchi, H.; Nozaki, K.; Tanaka, H., Distributed representation of fuzzy rules and its application to pattern classification, Fuzzy Sets Syst., 52, 21-32 (1992)
[28] Jabbar, M.; Deekshatulu, B.; Chandra, P., Knowledge discovery using associative classification for heart disease prediction, (Abraham, A.; Thampi, P., Intelligent Informatics (2013), Springer), 29-39
[29] Jiao, L.; Denoeux, T.; Pan, Q., A hybrid belief rule-based classification system based on uncertain training data and expert knowledge, IEEE Trans. Syst. Man Cybern.: Syst., 46, 1711-1723 (2016)
[30] Jiao, L.; Geng, X.; Pan, Q., Compact belief rule base learning for classification with evidential clustering, Entropy, 21, 1-16 (2019)
[31] Jiao, L.; Pan, Q.; Denoeux, T.; Liang, Y.; Feng, X., Belief rule-based classification system: extension of FRBCS in belief functions framework, Inf. Sci., 309, 26-49 (2015) · Zbl 1390.68535
[32] Li, W.; Han, J.; Pei, J., CMAR: accurate and efficient classification based on multiple-class association rule, in, (Proceedings of the International Conference on Data Mining (2001)), 369-376
[33] Liu, B.; Hsu, W.; Ma, Y., Integrating classificaiton and association rule mining, in, (Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (1998)), 337-341
[34] Lucas, J. P.; Laurent, A.; Moreno, M. N.; Teisseire, M., A fuzzy associative classification approach for recommender systems, Int. J. Uncertainty Fuzziness Knowl.-Based Syst., 20, 579-617 (2012)
[35] Nguyen, T.; Khosravi, A.; Creighton, D.; Nahavandi, S., Classification of healthcare data using genetic fuzzy logic system and wavelets, Expert Syst. Appl., 42, 2184-2197 (2015)
[36] Qiu, S.; Sallak, M.; Schön, W.; Ming, H. X., A valuation-based system approach for risk assessment of belief rule-based expert systems, Inf. Sci., 466, 323-336 (2018) · Zbl 1441.68250
[37] Sakai, H.; Nakata, M.; Watada, J., NIS-Apriori-based rule generation with three-way decisions and its application system in SQL, Inf. Sci., 507, 755-771 (2020) · Zbl 1456.68201
[38] Sanz, J. A.; Bernardo, D.; Herrera, F.; Bustince, H.; Hagras, H., A compact evolutionary interval-valued fuzzy rule-based classification system for the modeling and prediction of real-world financial applications with imbalanced data, IEEE Trans. Fuzzy Syst., 23, 973-990 (2015)
[39] Shafer, G., A Mathematical Theory of Evidence (1976), Princeton University Press: Princeton University Press Princeton, NJ · Zbl 0359.62002
[40] Smets, P., Decision making in the TBM: the necessity of the pignistic transformation, Int. J. Approximate Reasoning, 38, 133-147 (2005) · Zbl 1065.68098
[41] Thabtah, F., A review of associative classification mining, Knowl. Eng. Rev., 22, 37-65 (2007)
[42] Thabtah, F.; Cowling, P.; Peng, Y., MMAC: a new multi-class, multi-label associative classification approach, in, (Proceedings of the 4th IEEE International Conference on Data Mining (2004)), 217-224
[43] Torshizi, A. D.; Petzold, L.; Cohen, M., Multivariate soft repulsive system identification for constructing rule-based classification systems: application to trauma clinical data, Neurocomputing, 245, 77-85 (2017)
[44] Yang, J. B.; Liu, J.; Wang, J.; Sii, H. S.; Wang, H. W., Belief rule base inference methodology using the evidential reasoning approach-RIMER, IEEE Trans. Syst. Man Cybern.-Part A, 36, 266-285 (2006)
[45] L.H. Yang, J. Liu, Y.M. Wang, L. Martínez, A micro-extended belief rule-based system for big data multiclass classification problems, IEEE Trans. Syst. Man Cybern.: Syst. (2020) in press. https://doi.org/10.1109/TSMC.2018.2872843.
[46] Yin, X.; Han, J., CPAR: Classification based on predictive association rule, (Proceedings of the SIAM International Conference on Data Mining (2003)), 331-335
[47] Zadeh, L. A., Fuzzy sets, Inf. Control, 8, 338-353 (1965) · Zbl 0139.24606
[48] Zhou, K.; Martin, A.; Pan, Q., A belief combination rule for a large number of sources, J. Adv. Inf. Fusion, 14, 22-40 (2019)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.