Data Mining on the Sisyphus Dataset: Evaluation and Integration of Results

T Gartner, Wu Shaomin, PA Flach

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

This paper describes our work on the Sisyphus challenge dataset, which includes both classification and clustering tasks. We present our work in the context of the CRISP-DM methodology. Further key aspects of the work are the evaluation and integration of multiple models by means of ROC analysis. We indicate a simple method of forcing classifiers to cover the whole of the ROC space. In conclusion, we outline several promising research directions.
Translated title of the contributionData Mining on the Sisyphus Dataset: Evaluation and Integration of Results
Original languageEnglish
Title of host publicationUnknown
EditorsChristophe Giraud-Carrier, Nada Lavrac, Steve Moyle
PublisherECML/PKDD'01 workshop notes
Pages69 - 80
Number of pages11
Publication statusPublished - Sep 2001

Bibliographical note

Conference Proceedings/Title of Journal: Integrating Aspects of Data Mining, Decision Support and Meta-Learning

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