A Study on Two-Stage Multi-objective Fuzzy Genetics-based Machine Learning Using an Archive Population
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- Konishi Takeru
- Osaka Metropolitan University
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- Masuyama Naoki
- Osaka Metropolitan University
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- Nojima Yusuke
- Osaka Metropolitan University
Bibliographic Information
- Other Title
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- アーカイブ個体群を用いた2段階ファジィ遺伝的機械学習の検討
Abstract
<p>Multi-objective fuzzy genetics-based machine learning (MoFGBML), one of the most well-known multi-objective evolutionary fuzzy systems, can efficiently obtain a set of fuzzy classifiers considering the maximization of classification performance and the minimization of the model complexity. However, MoFGBML has a strong bias towards minimizing complexity in the search process, which makes it easy to obtain classifiers with low complexity but difficult to obtain classifiers with high classification performance. As a result, the number of non-dominated classifiers is often small. 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. In this paper we consider the use of an archive population in two-stage fuzzy genetics-based machine learning to further increase the number of non-dominated solutions and improve the tradeoff curve between two objectives.</p>
Journal
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- Proceedings of the Fuzzy System Symposium
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Proceedings of the Fuzzy System Symposium 39 (0), 666-671, 2023
Japan Society for Fuzzy Theory and Intelligent Informatics
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Details 詳細情報について
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- CRID
- 1390017611466853632
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- Text Lang
- ja
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- Data Source
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- JaLC
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- Abstract License Flag
- Disallowed