Decision support for data mining: introduction to ROC analysis  and its application

P.A. Flach; H. Blockeel; C. Ferri; J. Hernandez-Orallo; J. Struyf

Decision support for data mining: introduction to ROC analysis and its application

P.A. Flach, H. Blockeel, C. Ferri, J. Hernandez-Orallo, J. Struyf

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter in a book

Abstract

In this chapter we give an introduction to ROC (``receiver operating characteristics'') analysis and its applications to data mining. We argue that ROC analysis provides decision support for data mining in several ways. For model selection, ROC analysis establishes a method to determine the optimal model once the operating characteristics for the model deployment context are known. We also show how ROC analysis can aid in constructing and refining models in the modeling stage.

Translated title of the contribution	Decision support for data mining: introduction to ROC analysis and its application
Original language	English
Title of host publication	Data Mining and Decision Support: Aspects of Integration and Collaboration
Publisher	Kluwer Academic Publishers
ISBN (Print)	1402073887
Publication status	Published - 2003

Bibliographical note

Other page information: 81-90
Other identifier: 1000701

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http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=1000701

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Cite this

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title = "Decision support for data mining: introduction to ROC analysis and its application",

abstract = "In this chapter we give an introduction to ROC (``receiver operating characteristics'') analysis and its applications to data mining. We argue that ROC analysis provides decision support for data mining in several ways. For model selection, ROC analysis establishes a method to determine the optimal model once the operating characteristics for the model deployment context are known. We also show how ROC analysis can aid in constructing and refining models in the modeling stage. ",

author = "P.A. Flach and H. Blockeel and C. Ferri and J. Hernandez-Orallo and J. Struyf",

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year = "2003",

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T1 - Decision support for data mining: introduction to ROC analysis and its application

AU - Flach, P.A.

AU - Blockeel, H.

AU - Ferri, C.

AU - Hernandez-Orallo, J.

AU - Struyf, J.

N1 - Other page information: 81-90 Other identifier: 1000701

PY - 2003

Y1 - 2003

N2 - In this chapter we give an introduction to ROC (``receiver operating characteristics'') analysis and its applications to data mining. We argue that ROC analysis provides decision support for data mining in several ways. For model selection, ROC analysis establishes a method to determine the optimal model once the operating characteristics for the model deployment context are known. We also show how ROC analysis can aid in constructing and refining models in the modeling stage.

AB - In this chapter we give an introduction to ROC (``receiver operating characteristics'') analysis and its applications to data mining. We argue that ROC analysis provides decision support for data mining in several ways. For model selection, ROC analysis establishes a method to determine the optimal model once the operating characteristics for the model deployment context are known. We also show how ROC analysis can aid in constructing and refining models in the modeling stage.

M3 - Chapter in a book

SN - 1402073887

BT - Data Mining and Decision Support: Aspects of Integration and Collaboration

PB - Kluwer Academic Publishers