Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Daee, Pedram
dc.contributor.author Peltola, Tomi
dc.contributor.author Soare, Marta
dc.contributor.author Kaski, Samuel
dc.date.accessioned 2017-08-03T12:09:38Z
dc.date.available 2017-08-03T12:09:38Z
dc.date.issued 2017-07-12
dc.identifier.citation Daee , P , Peltola , T , Soare , M & Kaski , S 2017 , ' Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction ' Machine Learning , pp. 1-22 . DOI: 10.1007/s10994-017-5651-7 en
dc.identifier.issn 1573-0565
dc.identifier.other PURE UUID: 928fb288-edf4-4b37-a550-8578891eeb88
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/knowledge-elicitation-via-sequential-probabilistic-inference-for-highdimensional-prediction(928fb288-edf4-4b37-a550-8578891eeb88).html
dc.identifier.other PURE LINK: https://github.com/HIIT/knowledge-elicitation-for-linear-regression
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/14227776/10.1007_s10994_017_5651_7.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/27398
dc.description.abstract Prediction in a small-sized sample with a large number of covariates, the “small n, large p” problem, is challenging. This setting is encountered in multiple applications, such as in precision medicine, where obtaining additional data can be extremely costly or even impossible, and extensive research effort has recently been dedicated to finding principled solutions for accurate prediction. However, a valuable source of additional information, domain experts, has not yet been efficiently exploited. We formulate knowledge elicitation generally as a probabilistic inference process, where expert knowledge is sequentially queried to improve predictions. In the specific case of sparse linear regression, where we assume the expert has knowledge about the relevance of the covariates, or of values of the regression coefficients, we propose an algorithm and computational approximation for fast and efficient interaction, which sequentially identifies the most informative features on which to query expert knowledge. Evaluations of the proposed method in experiments with simulated and real users show improved prediction accuracy already with a small effort from the expert. en
dc.format.extent 22
dc.format.extent 1-22
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Machine Learning en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.title Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Computer Science
dc.subject.keyword Bayesian methods
dc.subject.keyword Experimental design
dc.subject.keyword Human-to-machine transfer learning
dc.subject.keyword Interactive machine learning
dc.subject.keyword Statistics in high dimensions
dc.subject.keyword 113 Computer and information sciences
dc.identifier.urn URN:NBN:fi:aalto-201708036366
dc.identifier.doi 10.1007/s10994-017-5651-7
dc.type.version publishedVersion


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