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TRCLA: A Transfer Learning Approach to Reduce Negative Transfer for Cellular Learning Automata.

Seyyed Amir Hadi Minoofam, Azam Bastanfard, Mohammad Reza Keyvanpour

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    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel transductive learning algorithm using cellular learning automata (CLA) to reduce negative transfers (NTs) in machine learning. The new approach enhances accuracy and minimizes NTs, improving model performance in real-world applications.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Traditional machine learning assumes identical data distributions, which often fails in real-world scenarios.
    • Transfer learning addresses distribution shifts but introduces negative transfer (NT) challenges.
    • Existing research inadequately addresses the significant problem of NTs.

    Purpose of the Study:

    • To propose a novel transductive learning algorithm to alleviate the negative transfer (NT) issue.
    • To introduce new decision criteria within cellular learning automata (CLA) to mitigate NTs.
    • To enhance the performance of machine learning models in non-identical data distribution environments.

    Main Methods:

    • Developed a transductive learning algorithm based on cellular learning automata (CLA).
    • Employed two established learning automata (LA) as estimator CLAs.
    • Introduced novel 'merit' and 'attitude' parameters to CLA for NT limitation.

    Main Results:

    • The proposed CLA-based algorithm demonstrated reduced negative transfer (NT).
    • Experimental results showed higher accuracy compared to existing methods.
    • The algorithm effectively addressed challenges posed by non-identical data distributions.

    Conclusions:

    • The novel transductive learning algorithm based on CLA successfully mitigates negative transfer.
    • The introduced decision criteria (merit and attitude parameters) are effective in limiting NTs.
    • This approach offers improved accuracy and robustness for machine learning in diverse data environments.