The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A regressão ordinal é usada para classificar instâncias considerando a relação ordinal entre os rótulos. Os métodos existentes tendem a diminuir a precisão quando aderem à preservação da relação ordinal. Portanto, propomos uma rede distributiva baseada em conhecimento (DK-net) que considera a relação ordinal enquanto mantém alta precisão. DK-net concentra-se em conjuntos de dados de imagens. Porém, em aplicações industriais, é possível encontrar não apenas dados de imagem, mas também dados tabulares. Neste estudo, propomos o conjunto de decisão inconsciente neural DK (NODE), uma versão melhorada da rede DK para dados tabulares. DK-NODE usa NODE para extração de recursos. Além disso, propomos um método de ajuste do parâmetro que controla o grau de conformidade com a relação ordinal. Experimentamos três conjuntos de dados: conjunto de dados WineQuality, Abalone e Eucalyptus. Os experimentos mostraram que o método proposto alcançou alta precisão e pequeno MAE em três conjuntos de dados. Notavelmente, o método proposto teve o menor MAE médio em todos os conjuntos de dados.
Yoshiyuki TAJIMA
Yokohama National University
Tomoki HAMAGAMI
Yokohama National University
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Yoshiyuki TAJIMA, Tomoki HAMAGAMI, "Ordinal Regression Based on the Distributional Distance for Tabular Data" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 3, pp. 357-364, March 2023, doi: 10.1587/transinf.2022EDP7071.
Abstract: Ordinal regression is used to classify instances by considering ordinal relation between labels. Existing methods tend to decrease the accuracy when they adhere to the preservation of the ordinal relation. Therefore, we propose a distributional knowledge-based network (DK-net) that considers ordinal relation while maintaining high accuracy. DK-net focuses on image datasets. However, in industrial applications, one can find not only image data but also tabular data. In this study, we propose DK-neural oblivious decision ensemble (NODE), an improved version of DK-net for tabular data. DK-NODE uses NODE for feature extraction. In addition, we propose a method for adjusting the parameter that controls the degree of compliance with the ordinal relation. We experimented with three datasets: WineQuality, Abalone, and Eucalyptus dataset. The experiments showed that the proposed method achieved high accuracy and small MAE on three datasets. Notably, the proposed method had the smallest average MAE on all datasets.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7071/_p
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@ARTICLE{e106-d_3_357,
author={Yoshiyuki TAJIMA, Tomoki HAMAGAMI, },
journal={IEICE TRANSACTIONS on Information},
title={Ordinal Regression Based on the Distributional Distance for Tabular Data},
year={2023},
volume={E106-D},
number={3},
pages={357-364},
abstract={Ordinal regression is used to classify instances by considering ordinal relation between labels. Existing methods tend to decrease the accuracy when they adhere to the preservation of the ordinal relation. Therefore, we propose a distributional knowledge-based network (DK-net) that considers ordinal relation while maintaining high accuracy. DK-net focuses on image datasets. However, in industrial applications, one can find not only image data but also tabular data. In this study, we propose DK-neural oblivious decision ensemble (NODE), an improved version of DK-net for tabular data. DK-NODE uses NODE for feature extraction. In addition, we propose a method for adjusting the parameter that controls the degree of compliance with the ordinal relation. We experimented with three datasets: WineQuality, Abalone, and Eucalyptus dataset. The experiments showed that the proposed method achieved high accuracy and small MAE on three datasets. Notably, the proposed method had the smallest average MAE on all datasets.},
keywords={},
doi={10.1587/transinf.2022EDP7071},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - Ordinal Regression Based on the Distributional Distance for Tabular Data
T2 - IEICE TRANSACTIONS on Information
SP - 357
EP - 364
AU - Yoshiyuki TAJIMA
AU - Tomoki HAMAGAMI
PY - 2023
DO - 10.1587/transinf.2022EDP7071
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2023
AB - Ordinal regression is used to classify instances by considering ordinal relation between labels. Existing methods tend to decrease the accuracy when they adhere to the preservation of the ordinal relation. Therefore, we propose a distributional knowledge-based network (DK-net) that considers ordinal relation while maintaining high accuracy. DK-net focuses on image datasets. However, in industrial applications, one can find not only image data but also tabular data. In this study, we propose DK-neural oblivious decision ensemble (NODE), an improved version of DK-net for tabular data. DK-NODE uses NODE for feature extraction. In addition, we propose a method for adjusting the parameter that controls the degree of compliance with the ordinal relation. We experimented with three datasets: WineQuality, Abalone, and Eucalyptus dataset. The experiments showed that the proposed method achieved high accuracy and small MAE on three datasets. Notably, the proposed method had the smallest average MAE on all datasets.
ER -