Post Mining of Diversified Multiple Decision Trees for Actionable Knowledge Discovery
Most data mining algorithms and tools when applied to industrial problems such as Customer Relationship Management, insurance and banking they stop search at producing actual applicable knowledge. Unlike these models, actionable knowledge discovery techniques are useful in pointing out customers who are likely attritors and loyal. However, actionable knowledge discovery techniques require human experts to postprocess the discovered knowledge manually. Postprocessing is one of the actionable knowledge discovery techniques which are effective in decision making and overcomes considerable inefficiency which leads to human errors that are inherent in the traditional data mining systems. Hence, decision trees are postprocessed which suggest cost effective actions in order to maximize the profit based objective function. In the proposed approach, an effective actionable knowledge discovery based classification algorithm namely Actionable Multiple Decision Trees (AMDT) is developed to improve the robustness and classification accuracy and tests are conducted on UCI German benchmark data.
KeywordsData mining actionable knowledge discovery Postprocessing Decision tree
Unable to display preview. Download preview PDF.
- 1.Yang, Q., Yin, J., Ling, C.X., Chen, T.: Postprocessing Decision Trees to Extract Actionable Knowledge. In: Proc. IEEE Conf. Data Mining (ICDM 2003), pp. 685–688 (2003)Google Scholar
- 3.Hu, H., Li, J., Wang, H., Daggard, G., Shi, M.: A maximally diversified multiple decision tree algorithm for microarray data classification. In: WISB 2006 Proceedings of the Workshop on Intelligent Systems for Bioinformatics, vol. 73 (2006)Google Scholar
- 4.Ling, C.X., Chen, T., Yang, Q., Cheng, J.: Mining Optimal Actions for Intelligent CRM. In: Proc. IEEE Int’l Conf. Data Mining, ICDM (2002)Google Scholar
- 5.Li, J., Liu, H.: Ensembles of Cascading Trees. In: Proc. IEEE Int’l Conf. Data Mining (ICDM 2003), pp. 585–588 (2003)Google Scholar
- 7.Lakshmi, B.N., Raghunandhan, G.H.: A Conceptual Overview of Data Mining. In: Proceedings of the National Conference on Innovations in Emerging Technology, pp. 27–32 (2011)Google Scholar
- 9.Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann (1993)Google Scholar
- 11.Breiman, L.: Bagging predictors. Machine Learning, 123–140 (1996)Google Scholar
- 12.Breiman, L.: Random forests–random features. Technical Report 567, University of California, Berkley (1999)Google Scholar
- 13.Freund, Y., Schapire, R.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
- 14.He, Z., Xu, X.: Datamining for actionable knowledge: A survey. In: CoRR (2005)Google Scholar
- 15.Hand, D., Mannila, H., Smith, P.: Principles of Data Mining. MIT Press, Cambridge (2001)Google Scholar
- 16.UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets/Statlog+German+Credit+Data