A new ensemble learning method based on learning automata

Improving the performance of machine learning algorithms has been always the topic of interest in data mining. The ensemble learning is one of the machine learning methods that, according to the subject literature, it yields better performance than the single base learner in the accuracy parameter. In the ensemble learning, all base learners are considered at the same level in terms of power and separation capabilities. However, whether the ensemble is made of homogeneous based learners or it is made of heterogeneous base learners, in either case, weaknesses and strengths of the base learners are ignored. To overcome this challenge, the stronger coefficient of influence should be assigned to stronger base learners and the lower coefficient of influence should be assigned to weaker base learners. However, given that the data is associated with uncertainty in the real-world issues, it is impossible to determine which base learner performs better than the others under these circumstances. Learning Automata is one of the desirable options of reinforcement learning subject literature to dealing with dynamic environments. The learning automata works by receiving feedback from the environment. In this paper, a method named LAbEL has been proposed which allows the assignment of the coefficient of influence to each base learner in the ensemble dynamically. Due to the use of learning automata, the proposed method works adjusted to the problem space conditions. The LAbEL is based on learning automata and according to its ability to dealing with dynamic environments, it is possible to apply it to issues where data has nonlinear and unpredictable behavior.