Interactive Machine Learning: A State of the Art Review
- URL: http://arxiv.org/abs/2207.06196v1
- Date: Wed, 13 Jul 2022 13:43:16 GMT
- Title: Interactive Machine Learning: A State of the Art Review
- Authors: Natnael A. Wondimu, C\'edric Buche and Ubbo Visser
- Abstract summary: We provide a comprehensive analysis of the state-of-the-art of interactive machine learning (iML)
Research works on adversarial black-box attacks and corresponding iML based defense system, exploratory machine learning, resource constrained learning, and iML performance evaluation are analyzed.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Machine learning has proved useful in many software disciplines, including
computer vision, speech and audio processing, natural language processing,
robotics and some other fields. However, its applicability has been
significantly hampered due its black-box nature and significant resource
consumption. Performance is achieved at the expense of enormous computational
resource and usually compromising the robustness and trustworthiness of the
model. Recent researches have been identifying a lack of interactivity as the
prime source of these machine learning problems. Consequently, interactive
machine learning (iML) has acquired increased attention of researchers on
account of its human-in-the-loop modality and relatively efficient resource
utilization. Thereby, a state-of-the-art review of interactive machine learning
plays a vital role in easing the effort toward building human-centred models.
In this paper, we provide a comprehensive analysis of the state-of-the-art of
iML. We analyze salient research works using merit-oriented and
application/task oriented mixed taxonomy. We use a bottom-up clustering
approach to generate a taxonomy of iML research works. Research works on
adversarial black-box attacks and corresponding iML based defense system,
exploratory machine learning, resource constrained learning, and iML
performance evaluation are analyzed under their corresponding theme in our
merit-oriented taxonomy. We have further classified these research works into
technical and sectoral categories. Finally, research opportunities that we
believe are inspiring for future work in iML are discussed thoroughly.
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