Ensemble probabilistic quantization encoding for information preservation of numerical variables in convolutional neural networks

Ensemble probabilistic quantization encoding for information preservation of numerical variables in convolutional neural networks

Abstract One-hot encoding is a prevalent method used to convert numeric variables into categorical variables. But one-hot encoding omits crucial quantitative data, which compromises the performance of convolutional neural networks (CNNs). This study introduces the ensemble probabilistic quantization...

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Bibliographic Details
Main Authors: Ki Yup Nam, Hyun-Woong Park, Yeongseop Lee, Hun-Young Jung, Taeseen Kang
Format: Article
Language:English
Published: Nature Portfolio 2025-02-01
Series:Scientific Reports
Subjects:
Categorical variables
Convolutional neural networks
Encoding methods
Numerical variables
Online Access:https://doi.org/10.1038/s41598-025-89430-1

Internet

https://doi.org/10.1038/s41598-025-89430-1

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