The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a higher resolution than individual classifiers. Random forest is one of the types of ensemble learning methods that have been considered more than other ensemble learning methods due to its simple structure, ease of understanding, as well as higher efficiency than similar methods. The ability and efficiency of classical methods are always influenced by the data. The capabilities of independence from the data domain, and the ability to adapt to problem space conditions, are the most challenging issues about the different types of classifiers. In this paper, a method based on learning automata is presented, through which the adaptive capabilities of the problem space, as well as the independence of the data domain, are added to the random forest to increase its efficiency. Using the idea of reinforcement learning in the random forest has made it possible to address issues with data that have a dynamic behaviour. Dynamic behaviour refers to the variability in the behaviour of a data sample in different domains. Therefore, to evaluate the proposed method, and to create an environment with dynamic behaviour, different domains of data have been considered. In the proposed method, the idea is added to the random forest using learning automata. The reason for this choice is the simple structure of the learning automata and the compatibility of the learning automata with the problem space. The evaluation results confirm the improvement of random forest efficiency.

中文翻译：

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一种基于学习自动机的新型随机森林算法


聚合基分类器的目标是获得比单个分类器具有更高分辨率的聚合分类器。随机森林是比其他集成学习方法更被考虑的集成学习方法类型之一，因为它结构简单、易于理解并且比类似方法具有更高的效率。经典方法的能力和效率总是受到数据的影响。独立于数据域的能力以及适应问题空间条件的能力是不同类型分类器最具挑战性的问题。本文提出了一种基于学习自动机的方法，通过将问题空间的自适应能力以及数据域的独立性添加到随机森林中以提高其效率。在随机森林中使用强化学习的想法使得解决具有动态行为的数据问题成为可能。动态行为是指数据样本在不同域中的行为的可变性。因此，为了评估所提出的方法并创建具有动态行为的环境，需要考虑不同的数据域。在所提出的方法中，使用学习自动机将该想法添加到随机森林中。这种选择的原因是学习自动机结构简单以及学习自动机与问题空间的兼容性。评估结果证实了随机森林效率的提高。