Precise and eloquent label information is fundamental for interpreting the underlying data distributions distinctively and training of supervised and semi-supervised learning models
adequately. But obtaining large amount of labeled data demands substantial manual effort. This obligation can be mitigated by acquiring labels of most informative data instances
using Active Learning. However labels received from humans are not always reliable and poses the risk of introducing noisy class labels which will degrade the efficacy of a model instead of its improvement. In this paper, we address the problem of annotating sensor data instances of various Activities of Daily Living (ADLs) in smart home context. We exploit the interactions between the users and annotators in terms of relationships spanning across spatial and temporal space which accounts for an activity as well. We propose a novel annotator selection model SocialAnnotator which exploits the interactions between the users and annotators and rank the annotators based on their level of correspondence. We also introduce a novel approach to measure this correspondence distance using the spatial and temporal information of interactions, type of the relationships and activities. We validate our proposed SocialAnnotator framework in smart environments achieving ≈ 84% statistical confidence in data annotation
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Active Deep Learning for Activity Recognition with Context Aware Annotator Selection
Machine learning models are bounded by the credibility of ground truth data used for both training and testing. Regardless of the problem domain, this ground truth annotation is objectively manual and tedious as it needs considerable amount of human intervention.
With the advent of Active Learning with multiple annotators, the burden can be somewhat mitigated by actively acquiring labels of most informative data instances. However, multiple annotators with varying degrees of expertise poses new set of challenges in terms of quality of the label received and availability of the annotator. Due to limited amount of ground truth information addressing the variabilities of Activity of Daily Living (ADLs), activity recognition models using wearable and mobile devices are still not robust enough for real-world deployment. In this paper, we propose an active learning combined deep model which updates its network parameters based on the optimization of a joint loss function. We then propose a novel annotator selection model by exploiting the relationships among the users while considering their heterogeneity with respect to their expertise, physical and spatial context. Our proposed model leverages model-free deep reinforcement learning in a partially observable environment setting to capture the actionreward interaction among multiple annotators. Our experiments in real-world settings exhibit that our active deep model converges to optimal accuracy with fewer labeled instances and achieves 8% improvement in accuracy in fewer iterations.
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- Award ID(s):
- 1750936
- NSF-PAR ID:
- 10144109
- Date Published:
- Journal Name:
- Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Anchorage, Alaska, August 2019
- Page Range / eLocation ID:
- 1862 to 1870
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3292500.3330688
