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Automatically Balancing Accuracy and Comprehensibility in Predictive Modeling
School of Business and Informatics, University of Borås, Sweden.
School of Business and Informatics, University of Borås, Sweden.
Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
2005 (engelsk)Inngår i: 2005 8th International Conference on Information Fusion (FUSION) Philadelphia, PA 25-28 July 2005: Volume 2 of 2, IEEE conference proceedings, 2005, s. 1554-1560Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

One specific problem, when performing predictive modeling, is the tradeoff between accuracy and comprehensibility. When comprehensible models are required this normally rules out high-accuracy techniques like neural networks and committee machines. Therefore, an automated choice of a standard technique, known to generally produce sufficiently accurate and comprehensible models, would be of great value. In this paper it is argued that this requirement is met by an ensemble of classifiers, followed by rule extraction. The proposed technique is demonstrated, using an ensemble of common classifiers and our rule extraction algorithm G-REX, on 17 publicly available data sets. The results presented demonstrate that the suggested technique performs very well. More specifically, the ensemble clearly outperforms the individual classifiers regarding accuracy, while the extracted models have accuracy similar to the individual classifiers. The extracted models are, however, significantly more compact than corresponding models created directly from the data set using he standard tool CART; thus providing higher comprehensibility.

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IEEE conference proceedings, 2005. s. 1554-1560
Identifikatorer
URN: urn:nbn:se:his:diva-2130DOI: 10.1109/ICIF.2005.1592040ISI: 000234830400207Scopus ID: 2-s2.0-33847128033ISBN: 0-7803-9287-6 ISBN: 0-7803-9286-8 ISBN: 978-07803-9286-1 OAI: oai:DiVA.org:his-2130DiVA, id: diva2:32406
Konferanse
2005 8th International Conference on Information Fusion, FUSION; Philadelphia, PA; 25 July 2005 through 28 July 2005
Tilgjengelig fra: 2008-02-08 Laget: 2008-02-08 Sist oppdatert: 2017-11-27bibliografisk kontrollert

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Niklasson, Lars

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