Abstract
In this paper, we tackle the problem of multi-rules based classification for evidential data, i.e., data where imperfection is modeled through the Evidence theory. In this setting, a new algorithm called EviRC is introduced. This method uses different pruning techniques to omit irrelevant rules and defines a new matching criteria between the rules and the instance to classify. The selected rules are then combined using the powerful combination rules of the Evidence theory. Extensive experiments were conducted on several data sets in order to evaluate the proposed method. The experiments produce interesting results in term of classification quality.
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References
Abidi, A., Elmi, S., Bach Tobji, M.A., HadjAli, A., Ben Yaghlane, B.: Skyline queries over possibilistic RDF data. Int. J. Approx. Reason. 93, 277–289 (2018)
Aggarwal, C.C.: Managing and Mining Uncertain Data. Springer Publishing Company, Incorporated (2009)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Proc. VLDB 1215, 487–499 (1994)
Antoine, V., Quost, B., Marie-Hélène, M., Denoeux, T.: CECM: Constrained evidential C-means algorithm. Computat. Stat. Data Anal. 56(4), 894–914 (2012)
Bach Tobji, M.A., Ben Yaghlane, B.: Extraction des itemsets fréquents à partir de données évidentielles: application à une base de données éducationnelles. vol. RNTI-E-21, pp. 211–232 (2011)
Bach Tobji, M.A., Ben Yaghlane, B., Mellouli, K.: A new algorithm for mining frequent itemsets from evidential databases. Proc. IPMU 8, 1535–1542 (2008)
Bahri, N., Bach Tobji, M.A.: On indexing evidential data. Int. J. Approx. Reason. 106, 63–87 (2019)
Bahri, N., Bach Tobji, M.A., Ben Yaghlane, B.: Evidential rule-based classifier (2020). https://gitlab.com/NassimBahri/evirc/
Bell, D.A., Guan, J.W., Lee, S.K.: Generalized union and project operations for pooling uncertain and imprecise information. Data Knowl. Eng. 18(2), 89–117 (1996)
Bosc, P., Pivert, O.: About projection-selection-join queries addressed to possibilistic relational databases. IEEE Trans. Fuzzy Syst. 13(1), 124–139 (2005)
Bousnina, F.E., Bach Tobji, M.A., Chebbah, M., Ben Yaghlane, B.: Modeling evidential databases as possible worlds. Int. J. Intell. Syst. 33(6), 1146–1164 (2018)
Bousnina, F.E., Elmi, S., Chebbah, M., Bach Tobji, M.A., HadjAli, A., Ben Yaghlane, B.: Skyline operator over tripadvisor reviews within the belief functions framework. In: Jallouli, R., Zaïane, O.R., Bach Tobji, M.A., Srarfi Tabbane, R., Nijholt, A. (eds.) ICDEc 2017. LNBIP, vol. 290, pp. 186–197. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62737-3_16
Chen, G., Liu, H., Yu, L., Wei, Q., Zhang, X.: A new approach to classification based on association rule mining. Decis. Support Syst. 42(2), 674–689 (2006)
Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)
Dempster, A.P.: A generalization of Bayesian inference. J. Roy. Stat. Soc. Ser. B Methodolog. 30(2), 205–247 (1968)
Denoeux, T.: A k-nearest neighbor classification rule based on dempster-shafer theory. IEEE Trans. Syst. Man Cybern. 25(5), 804–813 (1995)
Fagin, R., Halpern, J.Y.: A new approach to updating beliefs. In: Proceedings of UAI, pp. 347–374 (1991)
Hewawasam, K.R., Premaratne, K., Shyu, M.L.: Rule mining and classification in a situation assessment application: a belief-theoretic approach for handling data imperfections. IEEE Trans. Syst. Man Cybern B Cybern. 37(6), 1446–1459 (2007)
Lee, S.K.: An extended relational database model for uncertain and imprecise information. In: Proceedings of VLDB, pp. 211–220 (1992)
Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 369–376. IEEE (2001)
Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: KDD 1998, pp. 80–86 (1998)
Liu, B., Ma, Y., Wong, C.-K.: Classification using association rules: weaknesses and enhancements. In: Grossman, R.L., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R.R. (eds.) Data Mining for Scientific and Engineering Applications. MC, vol. 2, pp. 591–605. Springer, Boston, MA (2001). https://doi.org/10.1007/978-1-4615-1733-7_30
Quinlan, J.R.: C4.5: Programs for Machine Learning, 1 edn. Elsevier (1992)
Samet, A., Guyet, T., Negrevergne, B., Dao, T.-T., Hoang, T.N., Tho, M.C.H.B.: Expert opinion extraction from a biomedical database. In: Antonucci, A., Cholvy, L., Papini, O. (eds.) ECSQARU 2017. LNCS (LNAI), vol. 10369, pp. 135–145. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-61581-3_13
Samet, A., Lefèvre, E., Ben Yahia, S.: Evidential data mining: precise support and confidence. J. Intell. Inform. Syst. 47(1), 135–163 (2016). https://doi.org/10.1007/s10844-016-0396-5
Shafer, G.: A Mathematical Theory of Evidence, vol. 1. Princeton University Press, Princeton (1976)
Wickramarathne, T.L., Premaratne, K., Kubat, M., Jayaweera, D.: Cofids: a belief-theoretic approach for automated collaborative filtering. IEEE Trans. Knowl. Data Eng. 23(2), 175–189 (2011)
Tan, P.N., Steinbach, M., Karpatne, A., Kumar, V.: Introduction to Data Mining, 2 edn. Pearson (2018)
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Bahri, N., Bach Tobji, M.A., Ben Yaghlane, B. (2020). Rule-Based Classification for Evidential Data. In: Davis, J., Tabia, K. (eds) Scalable Uncertainty Management. SUM 2020. Lecture Notes in Computer Science(), vol 12322. Springer, Cham. https://doi.org/10.1007/978-3-030-58449-8_17
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