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Learning Decision Trees from Histogram Data
Stockholms universitet, Institutionen för data- och systemvetenskap.
Stockholms universitet, Institutionen för data- och systemvetenskap.
Stockholms universitet, Institutionen för data- och systemvetenskap.
2015 (English)In: Proceedings of the 2015 International Conference on Data Mining: DMIN 2015 / [ed] Robert Stahlbock, Gary M. Weiss, AAAI Press, 2015, p. 139-145Conference paper, Published paper (Refereed)
Abstract [en]

When applying learning algorithms to histogram data, bins of such variables are normally treated as separate independent variables. However, this may lead to a loss of information as the underlying dependencies may not be fully exploited. In this paper, we adapt the standard decision tree learning algorithm to handle histogram data by proposing a novel method for partitioning examples using binned variables. Results from employing the algorithm to both synthetic and real-world data sets demonstrate that exploiting dependencies in histogram data may have positive effects on both predictive performance and model size, as measured by number of nodes in the decision tree. These gains are however associated with an increased computational cost and more complex split conditions. To address the former issue, an approximate method is proposed, which speeds up the learning process substantially while retaining the predictive performance.

Place, publisher, year, edition, pages
AAAI Press, 2015. p. 139-145
Keywords [en]
Histogram Learning, Histogram Tree
National Category
Information Systems
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221497Scopus ID: 2-s2.0-85004097184ISBN: 978-1-60132-403-0 (print)OAI: oai:DiVA.org:kth-221497DiVA, id: diva2:1175296
Conference
11th International Conference on Data Mining (DMIN'15), Las Vegas, Nevada, USA, July 27-30, 2015
Note

QC 20180119

Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-01-19Bibliographically approved

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  • apa
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Language
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  • Other locale
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Output format
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