Regression with n → 1 by expert knowledge elicitation

Marta Soare, Muhammad Ammad-Ud-din, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

We consider regression under the "extremely small n large p" condition, where the number of samples n is so small compared to the dimensionality p that predictors cannot be estimated without prior knowledge. This setup occurs in personalized medicine, for instance, when predicting treatment outcomes for an individual patient based on noisy high-dimensional genomics data. A remaining source of information is expert knowledge, which has received relatively little attention in recent years. We formulate the inference problem of asking expert feedback on features on a budget, propose an elicitation strategy for a simple "small n" setting, and derive conditions under which the elicitation strategy is optimal. Experiments on simulated experts, both on synthetic and genomics data, demonstrate that the proposed strategy can drastically improve prediction accuracy.

Original languageEnglish
Title of host publicationProceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016
PublisherIEEE
Pages734-739
Number of pages6
ISBN (Electronic)9781509061662
DOIs
Publication statusPublished - 31 Jan 2017
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Machine Learning and Applications - Anaheim, United States
Duration: 18 Dec 201620 Dec 2016
Conference number: 15
http://www.icmla-conference.org/icmla16/

Conference

ConferenceIEEE International Conference on Machine Learning and Applications
Abbreviated titleICMLA
CountryUnited States
CityAnaheim
Period18/12/201620/12/2016
Internet address

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