A large amount of research in the field of online learning have focused on the problem of overcoming catastrophic forgetting, and few researches have focused on the classification of data stream with appropriate accuracy and running time. On the other hand, due to the volume and type of data stream, many traditional machine learning algorithms do not have the necessary efficiency when faced with it. Thus, in this paper, a novel model using reinforcement learning and stochastic gradient descent algorithm is presented for classification stream data with appropriate accuracy and running time. One of the important features of reinforcement learning is that the agent can adapt its behaviour gradually to the changes that occur and gradually add to its previous knowledge. In this research, because of the using of reinforcement learning and definition of reward, the agent has a better performance in the environment. The proposed algorithm has been tested on various data, including the dataset of human activity recognition, and compared with several incremental algorithms in terms of accuracy and running time. According to the experimental results, the proposed algorithm has the best performance both in terms of accuracy and running time compared to other incremental algorithms.
Farzaneh, S., & Salimisartaghti, J. (2023). Designing a Model for Data Stream Classification Using RL and SGD. Soft Computing Journal, (), -. doi: 10.22052/scj.2023.248781.1124
MLA
Samira Farzaneh; Javad Salimisartaghti. "Designing a Model for Data Stream Classification Using RL and SGD". Soft Computing Journal, , , 2023, -. doi: 10.22052/scj.2023.248781.1124
HARVARD
Farzaneh, S., Salimisartaghti, J. (2023). 'Designing a Model for Data Stream Classification Using RL and SGD', Soft Computing Journal, (), pp. -. doi: 10.22052/scj.2023.248781.1124
VANCOUVER
Farzaneh, S., Salimisartaghti, J. Designing a Model for Data Stream Classification Using RL and SGD. Soft Computing Journal, 2023; (): -. doi: 10.22052/scj.2023.248781.1124
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