Asterisk-Shaped Features for Tabular Data
Authors: 
Yuki Kurauchi, Yoshiaki Takimoto, Shuhei Yamamoto, Shunichi Seko, Hiroyuki TodaAuthors Info & Claims
Abstract
Data often accumulates in tabular format with many attribute items, and prediction using machine learning adds value to data for business. However, studies on machine learning for tabular data only input attribute values, which reduces accuracy. Therefore, we propose an inference method that inputs attribute values and values from aggregated tabular data that has varying attribute values for each attribute item. In an experiment, we compared our proposed method with AutoGluon-Tabular using AutoML benchmark datasets. Our proposed method achieved the highest accuracy for 21 out of 39 datasets.
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Index Terms
- Asterisk-Shaped Features for Tabular Data
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Information
Published In
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
- General Chairs:
- Gianluca Demartini,
- Guido Zuccon,
- Program Chairs:
- J. Shane Culpepper,
- Zi Huang,
- Hanghang Tong
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Published: 30 October 2021
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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management
November 1 - 5, 2021
Queensland, Virtual Event, Australia
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