Predicting the Benefit of Rule Extraction A Novel Component in Data Mining.
When performing data mining, the selection of data mining technique is a critical decision. Often this choice boils down to whether a transparent model is needed or not. Most research indicates that techniques producing transparent models, such as decision trees, often have an inferior accuracy comp...
| Publicado en: | Human IT Vol. 7; no. 3; pp. 78 - 109 |
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| Autores principales: | , |
| Formato: | Artículo |
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Human IT
2005
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=18021041&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 18021041 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 14021501 F9G jtl: Human IT issn: 14021501 maglogo: N pubinfo: dt: 2005 vid: 7 iid: 3 pid: 21835 pub: Human IT artinfo: ui: 18021041 ppf: 78 ppct: 31 formats: fmt: @attributes: type: P size: 294KB tig: atl: Predicting the Benefit of Rule Extraction A Novel Component in Data Mining. aug: au: Löfström, Tuve Johansson, Ulf affil: Department of Business and Informatics, University College of Borås Lecturer in informatics, Department of Business and Informatics, University College of Borås su: Data mining Artificial neural networks Artificial intelligence Computer engineering Information technology sug: subj: Data mining Artificial neural networks Artificial intelligence Computer engineering Information technology ab: When performing data mining, the selection of data mining technique is a critical decision. Often this choice boils down to whether a transparent model is needed or not. Most research indicates that techniques producing transparent models, such as decision trees, often have an inferior accuracy compared to techniques such as neural networks. On the other hand, models created by neural networks are opaque, which must be considered a serious drawback as they are to be used for decision making. As an alternative, many researchers have tried to reduce this accuracy vs. comprehensibility trade-off by converting the opaque, high accuracy model into a transparent model -- a technique termed rule extraction. In this paper, the question addressed is whether it is possible to predict, from the characteristics of a data set, if rule extraction is likely to produce an accurate model. The somewhat surprising answer, found from an empirical study conducted on several publicly available data sets, is that it is possible using only a few data set features. In addition, the study shows that the chosen representation is very important for the success of rule extraction. The results should be seen as steps in a direction towards a more automated data mining process. The overall ambition is to reduce the need for critical decisions having to be made early in the process and in an ad-hoc fashion. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Human IT is the property of Human IT and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Human IT holder: Human IT dt: @attributes: year: 2005 holdings: @attributes: islocal: N |
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