User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu

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Kuvaus

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.

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoIUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces
TilaJulkaistu - 8 maaliskuuta 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaINTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES - Tokyo, Japani
Kesto: 7 maaliskuuta 201811 maaliskuuta 2018
Konferenssinumero: 23
http://iui.acm.org/2018/index.html

Conference

ConferenceINTERNATIONAL CONFERENCE ON INTELLIGENT USER INTERFACES
LyhennettäIUI
MaaJapani
KaupunkiTokyo
Ajanjakso07/03/201811/03/2018
www-osoite

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