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Evaluation of a Variance-Based Nonconformity Measure for Regression Forests
Stockholms universitet, Samhällsvetenskapliga fakulteten, Institutionen för data- och systemvetenskap.
2016 (engelsk)Inngår i: Conformal and Probabilistic Prediction with Applications: 5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016, Proceedings / [ed] Alexander Gammerman, Zhiyuan Luo, Jesús Vega, Vladimir Vovk, Springer, 2016, 75-89 s.Konferansepaper, Publicerat paper (Fagfellevurdert)
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-the-art 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.

sted, utgiver, år, opplag, sider
Springer, 2016. 75-89 s.
Serie
Lecture Notes in Computer Science, ISSN 0302-9743 ; 9653
Emneord [en]
Conformal prediction, Nonconformity measures, Regression, Random forests
HSV kategori
Forskningsprogram
data- och systemvetenskap
Identifikatorer
URN: urn:nbn:se:su:diva-137476DOI: 10.1007/978-3-319-33395-3_6ISBN: 978-3-319-33394-6 (tryckt)ISBN: 978-3-319-33395-3 (tryckt)OAI: oai:DiVA.org:su-137476DiVA: diva2:1062751
Konferanse
5th International Symposium, COPA 2016, Madrid, Spain, April 20-22, 2016
Tilgjengelig fra: 2017-01-08 Laget: 2017-01-08 Sist oppdatert: 2017-01-11bibliografisk kontrollert

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