Abstract
The common methods for dealing with classification problems include data-driven models and knowledge-driven models. Recently, some methods were proposed to combine the data-driven model with the knowledge-driven model to construct a hybrid model, which improves the classification performance by complementing each other. However, most of the existing methods just assume that the expert knowledge is known in advance, and do not indicate how to obtain it. To this end, this paper proposes a way to obtain knowledge from experts represented by rules through active learning. Then, a hybrid rule-based classification model is developed by integrating the knowledge-driven rule base and the rule base learned from the training data using genetic algorithm. Experiments based on real datasets demonstrate the superiority of the proposed classification model.
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References
Abu Ghali, M.J., Mukhaimer, M.N., Abu Yousef, M.K., Abu-Naser, S.S.: Expert system for problems of teeth and gums. Int. J. Eng. Inform. Syst. 1(4), 198–206 (2017)
Alonso, J.M., Magdalena, L., Guillaume, S.: Designing highly interpretable fuzzy rule-based systems with integration of expert and induced knowledge. In: Proceedings of the 2008 International Conference on Information Processing & Management of Uncertainty in Knowledge-Based Systems, pp. 682–689. Springer, Málaga (2008)
Alonso, J.M.: Interpretable fuzzy systems modeling with cooperation between expert and induced knowledge. Ph.D. thesis, University of Santiago de Compostela, Santiago de Compostela (2007)
Alonso, J.M., Magdalena, L., Guillaume, S.: HILK: a new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism. Int. J. Intell. Syst. 23(7), 761–794 (2008)
Biçer, I., Sevis, D., Bilgiç, T.: Bayesian credit scoring model with integration of expert knowledge and customer data. In: Proceedings of the 24th Mini EURO Conference on Continuous Optimization and Information Technologies in the Financial Sector, pp. 324–329. Izmir University of Economics, Izmir (2010)
Cano, A., Masegosa, A.R., Moral, S.: A method for integrating expert knowledge when learning Bayesian networks from data. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 41(5), 1382–1394 (2011)
Chi, Z., Yan, H., Pham, T.: Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition. World Scientific, Singapore (1996)
Gou, J., Ma, H., Ou, W., Zeng, S., Rao, Y., Yang, H.: A generalized mean distance-based k-nearest neighbor classifier. Expert Syst. Appl. 115(1), 356–372 (2019)
Guillaume, S., Charnomordic, B.: Fuzzy inference systems: an integrated modeling environment for collaboration between expert knowledge and data using FisPro. Expert Syst. Appl. 39(10), 8744–8755 (2012)
Guillaume, S., Magdalena, L.: Expert guided integration of induced knowledge into a fuzzy knowledge base. Soft. Comput. 10(9), 773–784 (2006)
Guo, Y., Greiner, R.: Optimistic active-learning using mutual information. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, pp. 823–829. AAAI, Hyderabad (2007)
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(12), 1711–1723 (2015)
Liu, T.Y., Yang, Y., Wan, H., Zeng, H.J., Chen, Z., Ma, W.Y.: Support vector machines classification with a very large-scale taxonomy. ACM SIGKDD Explor. Newsl. 7(1), 36–43 (2005)
Ma, H., Jiao, L., Pan, Q.: Hybrid classification by integrating expert knowledge and data: literature review. In: Proceedings of the 40th Chinese Control Conference, pp. 3231–3235. IEEE, Shanghai (2021)
Meesad, P., Yen, G.G.: Combined numerical and linguistic knowledge representation and its application to medical diagnosis. IEEE Trans. Syst. Man Cybern. Part A (Syst. Hum.) 33(2), 206–222 (2003)
Mirčevska, V., Luštrek, M., Gams, M.: Combining machine learning and expert knowledge for classifying human posture. In: Proceedings of the 18th International Electrotechnical and Computer Science Conference, pp. 183–186. IEEE, Portorož (2009)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Elsevier, Amsterdam (2014)
Samhan, L.F., Alfarra, A.H., Abu-Naser, S.S.: An expert system for knee problems diagnosis. Int. J. Acad. Inf. Syst. Res. 5(4), 59–66 (2021)
Tang, W., Mao, K., Mak, L.O., Ng, G.W.: Adaptive fuzzy rule-based classification system integrating both expert knowledge and data. In: Proceedings of the 24th International Conference on Tools with Artificial Intelligence, pp. 814–821. IEEE, Athens (2012)
Trawiński, K., Cordón, O., Sánchez, L., Quirin, A.: A genetic fuzzy linguistic combination method for fuzzy rule-based multiclassifiers. IEEE Trans. Fuzzy Syst. 21(5), 950–965 (2013)
Yegnanarayana, B.: Artificial Neural Networks. PHI Learning Pvt. Ltd., Delhi (2009)
Zhou, Z.J., Hu, C.H., Yang, J.B., Xu, D.L., Chen, M.Y., Zhou, D.H.: A sequential learning algorithm for online constructing belief-rule-based systems. Expert Syst. Appl. 37(2), 1790–1799 (2010)
Zhou, Z.J., Hu, C.H., Yang, J.B., Xu, D.L., Zhou, D.H.: Online updating belief rule based system for pipeline leak detection under expert intervention. Expert Syst. Appl. 36(4), 7700–7709 (2009)
Acknowledgments
This work was funded by National Natural Science Foundation of China (Grant Nos. 62171386, 61801386 and 61790552), and China Postdoctoral Science Foundation (Grant Nos. 2019M653743 and 2020T130538).
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Jiao, L., Ma, H., Pan, Q. (2022). Hybrid Rule-Based Classification by Integrating Expert Knowledge and Data. In: Honda, K., Entani, T., Ubukata, S., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2022. Lecture Notes in Computer Science(), vol 13199. Springer, Cham. https://doi.org/10.1007/978-3-030-98018-4_17
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