×

Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. (English) Zbl 1230.90113

Summary: As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object set varies over time. Experimental results validate the feasibility of the incremental learning optimization.

MSC:

90B50 Management decision making, including multiple objectives

Software:

RRIA
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Bang W., Bien Z.: New incremental learning algorithm in the framework of rough set theory. Int. J. Fuzzy Syst. 1, 25–36 (1999)
[2] Baourakis G., Conisescu M., van Dijk G., Pardalos P.M., Zopounidis C.: A multicriteria approach for rating the credit risk of financial institutions. Comput. Manage. Sci. 6(3), 347–356 (2009) · Zbl 1169.91381 · doi:10.1007/s10287-007-0050-3
[3] Campana, E., Fasano, G., Pinto, A.: Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization. J. Glob. Optim. (2010). doi: 10.1007/s10898-009-9493-0 · Zbl 1206.90121
[4] Chan C.: A rough set approach to attribute generalization in data mining. Inf. Sci. 107, 177–194 (1998) · doi:10.1016/S0020-0255(97)10047-0
[5] Chen, H., Li, T., Liu, W. Zou, W.: Research on the approach of dynamically maintenance of approximations in rough set theory while attribute values coarsening and refining. In: Proceedings of 2009 IEEE International Conference on GrC, pp. 45–48 (2009)
[6] Chen H., Li T., Qiao S., Ruan D.: A rough set based dynamic maintenance approach for approximations in coarsening and refining attribute values. Int. J. Intell. Syst. 25(10), 1005–1026 (2010) · Zbl 1211.68420 · doi:10.1002/int.20436
[7] Cody W., Kreulen J., Krishna V., Spangler W.: The integration of business intellegence and knowledge management. IBM Syst. J. 41(4), 697–713 (2002) · Zbl 05420866 · doi:10.1147/sj.414.0697
[8] Floudas, C., Pardalos, P.M.: Encyclopedia of Optimization, 2nd ed., XXXIV, 4626 pp. Springer (2009) · Zbl 1156.90001
[9] Goyal M., Lu J., Zhang G.: Decision making in multi-issue e-market auction using fuzzy attitudes. J. Theor. Appl. Electron. Commer. Res. 3, 97–110 (2008) · doi:10.4067/S0718-18762008000100009
[10] Han J., Kamber M.: Data Mining: Concepts and Techniques. Corgan Kaufmann Press, San Fransisco (2006) · Zbl 1445.68004
[11] Hu, F.,Wang, G., Huang, H.,Wu, Y.: Incremental attribute reduction based on elementary sets. In: RSFDGrC2005, LNAI, vol. 3641, pp. 185–193 (2005) · Zbl 1134.68471
[12] Hu, J., Chang, H., Fu, M., Marcus, S.: Dynamic sample budget allocation in model-based optimization. J. Glob. Optim. (2009). doi: 10.1007/s10898-009-9493-0 · Zbl 1279.90163
[13] Jerzy B., Slowinski R.: Incremental induction of decision rules from dominance-based rough approximations. Electron. Notes Theor. Comput. Sci. 82, 40–51 (2003) · doi:10.1016/S1571-0661(04)80704-7
[14] Li T., Ruan D., Wets G., Song J., Xu Y.: A rough sets based characteristicrelation approach for dynamic attribute generalization in data mining. Knowl. Based Syst. 20, 485–494 (2007) · doi:10.1016/j.knosys.2007.01.002
[15] Liu D., Li T., Ruan D., Zou W.: An incremental approach for inducing knowledge from dynamic information systems. Fundam. Inform. 94, 245–260 (2009) · Zbl 1192.68530
[16] Liu, D., Li, T., Chen, H., Ji, X.: Approaches to knowledge incremental learning based on the changes of attribute values. In: Proceedings of the 4th International Conference on Intelligent Systems and Knowledge Engineering, pp. 94–99 (2009)
[17] Liu, D., Li, T., Liu, G., Hu, P.: An approach for inducing interesting incremental knowledge based on the change of attribute values. In: Proceedings of 2009 IEEE International Conference on Granular Computing, pp. 415–418 (2009)
[18] Liu, D., Zhang, J., Li, T.: A probabilistic rough set approach for incremental learning knowledge on the change of attribute. In: Proceedings of 2010 International Conference on Foundations and Applications of Computational Intelligence, pp. 722–727 (2010)
[19] Lu, J., Ruan, D., Zhang, G.: E-service intelligence-Methodology, Technologies and Applications, E-service Intelligence. pp. 1–33. Springer, (2007)
[20] Lu J., Bai C., Zhang G.: E-service cost benefit evaluation and analysis, E-Service Intelligence, pp. 389–409. Springer, New York (2007)
[21] Lu J., Bai C., Zhang G.: Cost-benefit factor analysis in e-services using bayesian networks. Expert Syst. Appl. 36, 4617–4625 (2009) · doi:10.1016/j.eswa.2008.05.018
[22] Pardalos, P.M., Hansen, P.: Data Mining and Mathematical Programming. American CRM Proceedings & Lecture Notes, vol. 45, 234 pp. American Mathematical Society (2008) · Zbl 1137.68011
[23] Pawlak Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982) · Zbl 0501.68053 · doi:10.1007/BF01001956
[24] Pawlak Z.: Rough set theory and its application to data analysis. Cybern. Syst. 29, 661–688 (1998) · Zbl 1008.03526 · doi:10.1080/019697298125470
[25] Qian Y., Liang J., Pedrycz Z., Dang C.: Positive approximation: an accelerator for attribute reduction rougt set theory. Artif. Intell. 174, 597–618 (2010) · Zbl 1205.68310 · doi:10.1016/j.artint.2010.04.018
[26] Shaku, A.: The top 10 critical challenges for business intelligence success. Retrieved from http://www.computerworld.com/computerworld/records/images/BusIntellWPonline.pdf
[27] Shan L., Ziarko W.: Data-based acquisition and incremental modification of classification rules. Comput. Intell. 11, 357–370 (1995) · doi:10.1111/j.1467-8640.1995.tb00038.x
[28] Sanati F., Lu J.: Life-event modelling framework for E-government integration. Electron. Gov Int. J. 7, 183–202 (2010)
[29] Tong, L.: An: incremental learning of decision rules based on rough set theory. In: Proceedings of the World Congress on Intelligent Control and Automation (WCICA2002), pp. 420–425 (2002)
[30] Tsumoto, S.: Extraction of experts’ decision process from clinical databases using rough set model. In: Proceedings of PKDD 1997, pp. 58–67 (1997)
[31] Tsumoto, S.: Accuracy and coverage in rough set rule induction. In: Alpigini, J. et al. (eds.) RSCTC 2002, LNAI, vol. 2475, pp. 373–380. (2002). · Zbl 1013.68567
[32] Wong S., Ziarko W., Pawlak A.: Algorithm for inductive learning. Bull. Pol. Acad. Sci. Tech. Sci. 34, 271–276 (1986)
[33] Yao Y., Wong S.: A decision theoretic framework for approximating concepts. Int. J. Man Mach. Stud. 37(6), 793–809 (1992) · doi:10.1016/0020-7373(92)90069-W
[34] Yao, Y.: A partition model of granular computing. Transactions on Rough Sets I, pp. 232–253 (2004) · Zbl 1104.68776
[35] Yao, Y.: Integrative levels of granularity. In: Human-Centric Information Processing Through Granular Modelling, pp. 31–47 (2009)
[36] Yao Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010) · Zbl 05663908 · doi:10.1016/j.ins.2009.09.021
[37] Zhang, G., Lu, J.: Fuzzy bilevel programming with multiple objectives and cooperative multiple followers. J. Glob. Optim. (2008). doi: 10.1007/s10898-008-9365-z · Zbl 1222.90058
[38] Zheng Z., Wang G.: RRIA: a rough set and rule tree based incremental knowledge acquisition algorithm. Fundam. Inform. 59, 299–313 (2004) · Zbl 1098.68711
[39] Ziarko W.: Variable precision rough set model. Comput. Syst. Sci. 46, 39–59 (1993) · Zbl 0764.68162 · doi:10.1016/0022-0000(93)90048-2
[40] Zopounidis, C., Pardalos, P.M.: Handbook of multicriteria analysis. Appl. Optim. 103, XXV, 455 pp (2010) · Zbl 1207.90001
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.