Abstract
Actionable knowledge discovery plays a vital role in industrial problems such as Customer Relationship Management, insurance and banking. Actionable knowledge discovery techniques are not only useful in pointing out customers who are loyal and likely attritors, but it also suggests actions to transform customers from undesirable to desirable. Postprocessing is one of the actionable knowledge discovery techniques which are efficient and effective in strategic decision making and used to unearth hidden patterns and unknown correlations underlying the business data. In this paper, we present a novel technique named Reordering based Diversified Actionable Decision Trees (RDADT), which is an effective actionable knowledge discovery based classification algorithm. RDADT contrasts traditional classification algorithms by constructing committees of decision trees in a reordered fashion and discover actionable rules containing all the attributes. Experimental evaluation on UCI benchmark data shows that the proposed technique has higher classification accuracy than traditional decision tree algorithms.
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References
Breiman, L.: Bagging predictors. In: Machine Learning, pp. 123–140 (1996)
Breiman, L.: Random forests–random features. Technical report 567, University of California, Berkley (1999)
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17, 299–310 (2005)
Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: International Conference on Machine Learning, pp. 148–156 (1996)
Han, J., Kamber, M., Pei, J.: Data Mining Concepts and Techniques, 2nd edn., Morgan Kaufmann (2006)
Domingos, P.: Metacost: a general method for making classifiers cost-sensitive. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 155–164. ACM (1999)
He, Z., Xu, X.: Datamining for actionable knowledge: a survey. In: CoRR (2005)
Pednault, E., Abe, N., Zadrozny, B.: Sequential cost-sensitive decision making with reinforcement learning. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259–268. ACM (2002)
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)
Li, J., Liu, H.: Ensembles of cascading trees. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2003), pp. 585–588 (2003)
Longbing, C.: Introduction to domain driven data mining. In: Data Mining for Business Applications, pp. 3–10. Springer (2009)
Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann (1993)
Cui, Z., Chen, W., He, Y., Chen, Y.: Optimal action extraction for random forests and boosted trees. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 179–188. ACM (2015)
Vadivu, P.S., David, V.K.: Optimized feature extraction and actionable knowledge discovery for Customer Relationship Management (CRM). In: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology, pp. 275–282. ACM (2012)
Yang, Q., Yin, J., Ling, C.X., Pan, R.: Extracting actionable knowledge decision trees. IEEE Trans. Knowl. Data Eng. 18(12), 43–56 (2007)
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Subramani, S. et al. (2016). Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_41
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DOI: https://doi.org/10.1007/978-3-319-48740-3_41
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