Mining actionable knowledge using reordering based diversified actionable decision trees

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dc.contributor.authorSubramani, S.
dc.contributor.authorWang, H.
dc.contributor.authorBalasubramaniam, S.
dc.contributor.authorZhou, R.
dc.contributor.authorMa, J.
dc.contributor.authorZhang, Y.
dc.contributor.authorWhittaker, F.
dc.contributor.authorZhao, Y.
dc.contributor.authorRangarajan, S.
dc.contributor.conference17th International Conference on Web Information Systems Engineering (WISE) (7 Nov 2016 - 10 Nov 2016 : Shanghai, China)
dc.contributor.editorCellary, W.
dc.contributor.editorMokbel, M.
dc.contributor.editorWang, J.
dc.contributor.editorWang, H.
dc.contributor.editorZhou, R.
dc.contributor.editorZhang, Y.
dc.date.issued2016
dc.description.abstractActionable 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.
dc.description.statementofresponsibilitySudha Subramani, Hua Wang, Sathiyabhama Balasubramaniam, Rui Zhou, Jiangang Ma, Yanchun Zhang, Frank Whittaker, Yueai Zhao, and Sarathkumar Rangarajan
dc.identifier.citationLecture Notes in Artificial Intelligence, 2016 / Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (ed./s), vol.10041, pp.553-560
dc.identifier.doi10.1007/978-3-319-48740-3_41
dc.identifier.isbn9783319487397
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.orcidZhou, R. [0000-0001-6807-4362]
dc.identifier.urihttp://hdl.handle.net/2440/109516
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofseriesLNCS
dc.rights© Springer International Publishing AG 2016
dc.source.urihttps://doi.org/10.1007/978-3-319-48740-3_41
dc.subjectData mining; Actionable knowledge discovery; Postprocessing; Decision tree
dc.titleMining actionable knowledge using reordering based diversified actionable decision trees
dc.typeConference paper
pubs.publication-statusPublished

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