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Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems
یادگیری تقویتی عمیق یک شات برای طبقه بندی هوشمندانه مصنوعی در سیستم های خبره-2020 In recent years there has been a sharp rise in applications, in which significant events need to be classified
but only a few training instances are available. These are known as cases of one-shot learning. To handle this
challenging task, organizations often use human analysts to classify events under high uncertainty. Existing
algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it
to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it
does not take the practical temporal constraints of system resources into account. By contrast, the focus in this
paper is on rigorously optimizing the resource consumption in the system which applies to broad application
domains, and is of a significant interest for academic research, industrial developments, as well as society
and citizens benefit. The contribution of this paper is threefold. First, a novel Deep Reinforcement One-shot
Learning (DeROL) framework is developed to address this challenge. The basic idea of the DeROL algorithm
is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data.
Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based
on the trained deep-Q network, to maximize the objective function. Second, the first open-source software
for practical artificially intelligent one-shot classification systems with limited resources is developed for the
benefit of researchers and developers in related fields. Third, an extensive experimental study is presented
using the OMNIGLOT dataset for computer vision tasks, the UNSW-NB15 dataset for intrusion detection tasks,
and the Cleveland Heart Disease Dataset for medical monitoring tasks that demonstrates the versatility and
efficiency of the DeROL framework. Keywords: Deep reinforcement learning | One-shot learning | Network optimization | Online classification |
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