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Evaluation of a variance-based nonconformity measure for regression forests
Department of Computer and Systems Sciences, Stockholm University, Kista, Sweden.
Department of Information Technology, University of Borås, Borås, Sweden.
Department of Information Technology, University of Borås, Borås, Sweden.ORCID iD: 0000-0003-0274-9026
Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics.ORCID iD: 0000-0003-0412-6199
2016 (English)In: Conformal and Probabilistic Prediction with Applications, Springer, 2016, 75-89 p.Conference paper, Published paper (Refereed)
Abstract [en]

In a previous large-scale empirical evaluation of conformal regression approaches, random forests using out-of-bag instances for calibration together with a k-nearest neighbor-based nonconformity measure, was shown to obtain state-of-the-art performance with respect to efficiency, i.e., average size of prediction regions. However, the use of the nearest-neighbor procedure not only requires that all training data have to be retained in conjunction with the underlying model, but also that a significant computational overhead is incurred, during both training and testing. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. Moreover, the evaluation shows that state-of-theart performance is achieved by the variance-based measure at a computational cost that is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure. 

Place, publisher, year, edition, pages
Springer, 2016. 75-89 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9653
Keyword [en]
Conformal prediction, Nonconformity measures, Random forests, Regression, Decision trees, Efficiency, Nearest neighbor search, Regression analysis, Computational overheads, Conformal predictions, Empirical evaluations, State-of-the-art performance, Training and testing, Forecasting
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hj:diva-38115DOI: 10.1007/978-3-319-33395-3_6Scopus ID: 2-s2.0-84964088557ISBN: 9783319333946 (print)OAI: oai:DiVA.org:hj-38115DiVA: diva2:1163934
Conference
5th International Symposium on Conformal and Probabilistic Prediction with Applications, COPA 2016; Madrid; Spain; 20 April 2016 through 22 April 2016
Available from: 2017-12-08 Created: 2017-12-08 Last updated: 2018-01-13Bibliographically approved

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Löfström, TuveJohansson, Ulf

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