Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction.
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| Title: | Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction. |
|---|---|
| Authors: | Daee, Pedram1, Peltola, Tomi1, Soare, Marta1, Kaski, Samuel1 Samuel.Kaski@aalto.fi |
| Source: | Machine Learning. Sep/Oct2017, Vol. 106 Issue 9-10, p1599-1620. 22p. |
| Subjects: | Probabilistic inference, Bayesian analysis, Human-machine systems, Machine learning, Sparse approximations |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Learning is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10994-017-5651-7 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1599 Subjects: – SubjectFull: Probabilistic inference Type: general – SubjectFull: Bayesian analysis Type: general – SubjectFull: Human-machine systems Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Sparse approximations Type: general Titles: – TitleFull: Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Daee, Pedram – PersonEntity: Name: NameFull: Peltola, Tomi – PersonEntity: Name: NameFull: Soare, Marta – PersonEntity: Name: NameFull: Kaski, Samuel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep/Oct2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 08856125 Numbering: – Type: volume Value: 106 – Type: issue Value: 9-10 Titles: – TitleFull: Machine Learning Type: main |
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