Verification of the Effectiveness of Using an Archive Population on Two-Stage Fuzzy Genetics-Based Machine Learning

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  • 2段階ファジィ遺伝的機械学習におけるアーカイブ個体群利用の効果検証

Abstract

<p>Multi-objective fuzzy genetics-based machine learning can efficiently obtain a set of fuzzy classifiers considering the maximization of classification performance and the minimization of the model complexity by using an evolutionary multi-objective optimization method. However, multi-objective fuzzy genetics-based machine learning has a strong bias towards minimizing complexity in the optimization process, making it difficult to generate classifiers with high classification performance. In our previous study, two-stage fuzzy genetics-based machine learning has been proposed to mitigate this bias: first, an accuracy-oriented single-objective optimization is performed, and then a multi-objective optimization is performed to maximize the classification performance and minimize the complexity. The use of an archive population has also been proposed to obtain a better set of classifiers in two-stage fuzzy genetics-based machine learning. However, the effects of the use of an archive population on a set of classifiers obtained by two-stage fuzzy genetics-based machine learning have not been fully investigated. In this paper, we investigate the effects through computational experiments on a wide variety of real-world datasets.</p>

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