IEICE TRANSACTIONS on Information
Ordinal Regression Based on the Distributional Distance for Tabular Data
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E106-D No.3 pp.357-364
- Publication Date
- 2023/03/01
- Publicized
- 2022/12/16
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2022EDP7071
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Yoshiyuki TAJIMA
Yokohama National University
Tomoki HAMAGAMI
Yokohama National University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
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
Copy
@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},}
Copy
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 -
English
