Abstract
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.
Highlights
Random forest is one of the methods of ensemble learning that comes under the homogeneous base learner category in terms of the type of constructive classifiers
All base learners are decision trees, and they have a simpler structure than similar methods [1]. e random forest structure has two advantages. e first category is from a computational point of view, and the second category is from a statistical point of view
Second point: if the situation changes due to the dynamics of the problem, the structure of the ensemble will change in such a way that it has the greatest amount of compatibility with the conditions of the new environment. erefore, in order to meet the above points and achieve an ensemble that is able to adapt to the dynamic conditions of the problem, in this paper, a new method based on the idea of reinforcement learning is proposed to integrate the base learners in the random forest
Summary
Random forest is one of the methods of ensemble learning that comes under the homogeneous base learner category in terms of the type of constructive classifiers. E train and prediction processes in this classifier are performed at high speed, and the random forest is known as one of the fast classic classifiers. Advantages that can be considered from a computational point of view are: the random forest has the ability to deal with both regression and classification issues. Another advantage of the random forest is its ability to be used directly in high-dimensional issues [2]. E advantages of the second view of the random forest are its characteristics, namely, prioritization of features, attribution of different weight coefficients to different classes, and illustration and unsupervised learning ability. Weighting the base learners in ensemble learning is one of the main challenges in aggregating the basic classifiers in order to achieve a stronger classifier [3]. e reason for weighing base learners, or in other words, determining the impact factor for each base learner, is to increase the scalability of the data mining algorithm with the problem space
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