Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets

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

Details

Original languageEnglish
Title of host publicationIUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces
Pages547-552
Number of pages6
ISBN (Electronic)9781450343480
StatePublished - 7 Mar 2017
MoE publication typeB3 Non-refereed article in conference proceedings
EventInternational Conference on Intelligent User Interfaces - Limassol, Cyprus
Duration: 13 Mar 201716 Mar 2017
Conference number: 22

Conference

ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
CountryCyprus
CityLimassol
Period13/03/201716/03/2017

Researchers

Research units

Abstract

Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain.

    Research areas

  • Interactive knowledge elicitation, Prediction, User model

ID: 11610075