Mining actionable knowledge using reordering based diversified actionable decision trees
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Date
2016
Authors
Subramani, S.
Wang, H.
Balasubramaniam, S.
Zhou, R.
Ma, J.
Zhang, Y.
Whittaker, F.
Zhao, Y.
Rangarajan, S.
Editors
Cellary, W.
Mokbel, M.
Wang, J.
Wang, H.
Zhou, R.
Zhang, Y.
Mokbel, M.
Wang, J.
Wang, H.
Zhou, R.
Zhang, Y.
Advisors
Journal Title
Journal ISSN
Volume Title
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Conference paper
Citation
Lecture Notes in Artificial Intelligence, 2016 / Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (ed./s), vol.10041, pp.553-560
Statement of Responsibility
Sudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Jiangang Ma, Yanchun Zhang, Frank Whittaker, Yueai Zhao, and Sarathkumar Rangarajan
Conference Name
17th International Conference on Web Information Systems Engineering (WISE) (7 Nov 2016 - 10 Nov 2016 : Shanghai, China)
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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Dissertation Note
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© Springer International Publishing AG 2016