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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussavertaisarvioitu




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.


OtsikkoIUI 2017 - Proceedings of the 22nd International Conference on Intelligent User Interfaces
TilaJulkaistu - 7 maaliskuuta 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Intelligent User Interfaces - Limassol, Kypros
Kesto: 13 maaliskuuta 201716 maaliskuuta 2017
Konferenssinumero: 22


ConferenceInternational Conference on Intelligent User Interfaces

ID: 11610075