Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance

Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance

Classifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains. This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of dif...

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Bibliographic Details
Main Authors: Ruiao Zou, Nan Wang
Format: Article
Language:English
Published: MDPI AG 2024-10-01
Series:Applied Sciences
Subjects:
imbalanced data
analysis of variance
parameter optimization
fractional factorial design
Online Access:https://www.mdpi.com/2076-3417/14/19/9071
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