User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction

TL;DR

This paper introduces a user modeling approach to prevent overfitting in interactive machine learning by inferring user knowledge, demonstrated through a sentiment analysis task with improved predictive performance.

Contribution

The paper proposes a probabilistic user modeling methodology to mitigate overfitting caused by user interaction in human-in-the-loop machine learning systems.

Findings

User modeling improves predictive accuracy in sentiment analysis.

The method effectively guards against overfitting caused by noisy user input.

Empirical validation with 48 participants supports the approach's effectiveness.

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.