Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/109516
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Type: Conference paper
Title: Mining actionable knowledge using reordering based diversified actionable decision trees
Author: Subramani, S.
Wang, H.
Balasubramaniam, S.
Zhou, R.
Ma, J.
Zhang, Y.
Whittaker, F.
Zhao, Y.
Rangarajan, S.
Citation: Web Information Systems Engineering, 2016 / Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (ed./s), vol.10041, pp.553-560
Publisher: Springer
Issue Date: 2016
Series/Report no.: LNCS
ISBN: 9783319487397
ISSN: 0302-9743
1611-3349
Conference Name: 17th International Conference on Web Information Systems Engineering (WISE) (07 Nov 2016 - 10 Nov 2016 : Shanghai, China)
Statement of
Responsibility: 
Sudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Jiangang Ma, Yanchun Zhang, Frank Whittaker, Yueai Zhao, and Sarathkumar Rangarajan
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.
Keywords: Data mining; Actionable knowledge discovery; Postprocessing; Decision tree
Rights: © Springer International Publishing AG 2016
RMID: 0030068759
DOI: 10.1007/978-3-319-48740-3_41
Appears in Collections:Computer Science publications

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