Persistent Link:
http://hdl.handle.net/10150/105859
Title:
A Decision-Theoretic Approach to Data Mining
Author:
Elovici, Yuval; Braha, Dan
Citation:
A Decision-Theoretic Approach to Data Mining 2003, 33(1):1-10 IEEE Transactions on Systems, Man, and Cybernetics. Part A.
Journal:
IEEE Transactions on Systems, Man, and Cybernetics. Part A.
Issue Date:
2003
URI:
http://hdl.handle.net/10150/105859
Submitted date:
2005-10-08
Abstract:
In this paper, we develop a decision-theoretic framework for evaluating data mining systems, which employ classification methods, in terms of their utility in decision-making. The decision-theoretic model provides an economic perspective on the value of â extracted knowledge,â in terms of its payoff to the organization, and suggests a wide range of decision problems that arise from this point of view. The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make is formalized. We propose two ways by which independent data mining systems can be combined and show that the combined data mining system can be used in the decision-making process of the organization to increase payoff. Examples are provided to illustrate the various concepts, and several ways by which the proposed framework can be extended are discussed.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Information Extraction; Data Mining; Interdisciplinarity; Learning Science; Information Analysis; Information Systems; Classification; Information Science; Economics of Information; Computer Science; Artificial Intelligence; Evaluation
Local subject classification:
actionability; classification; data mining; data mining economics; decision-making; knowledge discovery systems; decision making

Full metadata record

DC FieldValue Language
dc.contributor.authorElovici, Yuvalen_US
dc.contributor.authorBraha, Danen_US
dc.date.accessioned2005-10-08T00:00:01Z-
dc.date.available2010-06-18T23:35:42Z-
dc.date.issued2003en_US
dc.date.submitted2005-10-08en_US
dc.identifier.citationA Decision-Theoretic Approach to Data Mining 2003, 33(1):1-10 IEEE Transactions on Systems, Man, and Cybernetics. Part A.en_US
dc.identifier.urihttp://hdl.handle.net/10150/105859-
dc.description.abstractIn this paper, we develop a decision-theoretic framework for evaluating data mining systems, which employ classification methods, in terms of their utility in decision-making. The decision-theoretic model provides an economic perspective on the value of â extracted knowledge,â in terms of its payoff to the organization, and suggests a wide range of decision problems that arise from this point of view. The relation between the quality of a data mining system and the amount of investment that the decision maker is willing to make is formalized. We propose two ways by which independent data mining systems can be combined and show that the combined data mining system can be used in the decision-making process of the organization to increase payoff. Examples are provided to illustrate the various concepts, and several ways by which the proposed framework can be extended are discussed.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.subjectInformation Extractionen_US
dc.subjectData Miningen_US
dc.subjectInterdisciplinarityen_US
dc.subjectLearning Scienceen_US
dc.subjectInformation Analysisen_US
dc.subjectInformation Systemsen_US
dc.subjectClassificationen_US
dc.subjectInformation Scienceen_US
dc.subjectEconomics of Informationen_US
dc.subjectComputer Scienceen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectEvaluationen_US
dc.subject.otheractionabilityen_US
dc.subject.otherclassificationen_US
dc.subject.otherdata miningen_US
dc.subject.otherdata mining economicsen_US
dc.subject.otherdecision-makingen_US
dc.subject.otherknowledge discovery systemsen_US
dc.subject.otherdecision makingen_US
dc.titleA Decision-Theoretic Approach to Data Miningen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalIEEE Transactions on Systems, Man, and Cybernetics. Part A.en_US
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