Multi-Class Logistic Regression by Randomization and Nonlinearization

Masaaki Ida

doi:10.3156/jsoft.38.1_515

February 14, 2026Journal of Japan Society for Fuzzy Theory and Intelligent InformaticsOpen Access

Multi-Class Logistic Regression by Randomization and Nonlinearization

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Key Points

Improved accuracy in multi-class logistic regression results from randomization and nonlinearization techniques, enhancing data interpretability.
In specific numeric examples, the joint effects of high-dimensional randomization and nonlinearization alter the feature space characteristics significantly.
Verification was conducted using metrics like matrix rank and eigenvalue distribution, indicating desirable properties in the feature space were achieved.
Indicating a potential shift in how multi-class models can be effectively structured in complex data environments.

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Implication

Observational analysis demonstrates improved accuracy in multi-class logistic regression through novel feature space generation, suggesting effective data modeling methods.

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Masaaki Ida (2026) studied this question.

synapsesocial.com/papers/69a76136c6e9836116a2eecf https://doi.org/10.3156/jsoft.38.1_515

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Multi-Class Logistic Regression by Randomization and Nonlinearization

Key Points

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Implication

Abstract

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Multi-Class Logistic Regression by Randomization and Nonlinearization

Key Points

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Abstract

Discussion

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