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Assessor Feedback Mechanism for Machine Learning Model

by Musulmon Lolaev, Anand Paul*, Jeonghong Kim

The School of Computer Science and Engineering, Kyungpook National University, Dae-Hak ro, Daegu, 41566, Republic of Korea

* Corresponding Author: Anand Paul. Email: email

(This article belongs to the Special Issue: Security, Privacy, and Robustness for Trustworthy AI Systems)

Computers, Materials & Continua 2024, 81(3), 4707-4726. https://doi.org/10.32604/cmc.2024.058675

Abstract

Evaluating artificial intelligence (AI) systems is crucial for their successful deployment and safe operation in real-world applications. The assessor meta-learning model has been recently introduced to assess AI system behaviors developed from emergent characteristics of AI systems and their responses on a test set. The original approach lacks covering continuous ranges, for example, regression problems, and it produces only the probability of success. In this work, to address existing limitations and enhance practical applicability, we propose an assessor feedback mechanism designed to identify and learn from AI system errors, enabling the system to perform the target task more effectively while concurrently correcting its mistakes. Our empirical analysis demonstrates the efficacy of this approach. Specifically, we introduce a transition methodology that converts prediction errors into relative success, which is particularly beneficial for regression tasks. We then apply this framework to both neural network and support vector machine models across regression and classification tasks, thoroughly testing its performance on a comprehensive suite of 30 diverse datasets. Our findings highlight the robustness and adaptability of the assessor feedback mechanism, showcasing its potential to improve model accuracy and reliability across varied data contexts.

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APA Style
Lolaev, M., Paul, A., Kim, J. (2024). Assessor feedback mechanism for machine learning model. Computers, Materials & Continua, 81(3), 4707–4726. https://doi.org/10.32604/cmc.2024.058675
Vancouver Style
Lolaev M, Paul A, Kim J. Assessor feedback mechanism for machine learning model. Comput Mater Contin. 2024;81(3):4707–4726. https://doi.org/10.32604/cmc.2024.058675
IEEE Style
M. Lolaev, A. Paul, and J. Kim, “Assessor Feedback Mechanism for Machine Learning Model,” Comput. Mater. Contin., vol. 81, no. 3, pp. 4707–4726, 2024. https://doi.org/10.32604/cmc.2024.058675



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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