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Supporting Feature Engineering in End-User Machine Learning

McCallum, Louis and Fiebrink, Rebecca. 2019. 'Supporting Feature Engineering in End-User Machine Learning'. In: CHI 2019 Workshop on Emerging Perspectives in Human-Centered Machine Learning. Glasgow, United Kingdom 4 May 2019. [Conference or Workshop Item]

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Abstract or Description

A truly human-centred approach to Machine Learning (ML) must consider how to support people modelling phenomena beyond those receiving the bulk of industry and academic attention, including phenomena relevant only to niche communities and for which large datasets may never exist. While deep feature learning is often viewed as a panacea that obviates the task of feature engineering, it may be insufficient to support users with small datasets, novel data sources, and unusual learning problems. We argue that it is therefore necessary to investigate how to support users who are not ML experts in deriving suitable feature representations for new ML problems. We also report on the results of a preliminary study comparing user-driven and automated feature engineering approaches in a sensor-based gesture recognition task.

Item Type:

Conference or Workshop Item (Paper)

Identification Number (DOI):

https://doi.org/10.475/123_4

Keywords:

interactive machine learning, human-centred machine learning, feature engineering

Related URLs:

Departments, Centres and Research Units:

Computing
Computing > Embodied AudioVisual Interaction Group (EAVI)

Dates:

DateEvent
5 March 2019Accepted

Event Location:

Glasgow, United Kingdom

Date range:

4 May 2019

Item ID:

26117

Date Deposited:

28 Mar 2019 13:03

Last Modified:

21 May 2019 08:45

URI:

http://research.gold.ac.uk/id/eprint/26117

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