User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Details

Original languageEnglish
Title of host publicationIUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces
PublisherAssociation for Computing Machinery (ACM)
Pages305-310
Number of pages6
ISBN (Electronic)978-1-4503-4945-1
StatePublished - 8 Mar 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Intelligent User Interfaces - Tokyo, Japan
Duration: 7 Mar 201811 Mar 2018
Conference number: 23

Conference

ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
CountryJapan
CityTokyo
Period07/03/201811/03/2018

Researchers

Research units

Abstract

In human-in-the-loop machine learning, the user provides information beyond that in the training data. Many algorithms and user interfaces have been designed to optimize and facilitate this human--machine interaction; however, fewer studies have addressed the potential defects the designs can cause. Effective interaction often requires exposing the user to the training data or its statistics. The design of the system is then critical, as this can lead to double use of data and overfitting, if the user reinforces noisy patterns in the data. We propose a user modelling methodology, by assuming simple rational behaviour, to correct the problem. We show, in a user study with 48 participants, that the method improves predictive performance in a sparse linear regression sentiment analysis task, where graded user knowledge on feature relevance is elicited. We believe that the key idea of inferring user knowledge with probabilistic user models has general applicability in guarding against overfitting and improving interactive machine learning.

    Research areas

  • Interactive machine learning, Probabilistic modeling, Bayesian Inference, overfitting, expert prior elicitation, human-in-the-loop machine learning

ID: 15805147