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Feature Selection Method with Feature Subcategorization in Regression
http://hdl.handle.net/2237/0002013884
http://hdl.handle.net/2237/0002013884ea5a139b-e865-438f-9e98-44300a1664ad
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
WanwanZheng_Feature_selection_with_subcategorization.pdf (1.1 MB)
Download is available from 2026/5/3.
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| アイテムタイプ | itemtype_ver1(1) | |||||||
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| 公開日 | 2026-01-27 | |||||||
| タイトル | ||||||||
| タイトル | Feature Selection Method with Feature Subcategorization in Regression | |||||||
| 言語 | en | |||||||
| 著者 |
Zheng, Wanwan
× Zheng, Wanwan
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| アクセス権 | ||||||||
| アクセス権 | embargoed access | |||||||
| アクセス権URI | http://purl.org/coar/access_right/c_f1cf | |||||||
| 権利 | ||||||||
| 権利情報 | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s42979-025-03982-7 | |||||||
| 言語 | en | |||||||
| 内容記述 | ||||||||
| 内容記述タイプ | Abstract | |||||||
| 内容記述 | Feature selection plays a critical role in machine learning, yet existing methods primarily focus on generating “feature rankings" or “valid feature sets” without providing insights into the distinct roles of different features. This study proposes a feature selection method that categorizes features into four categories: relevant, interaction, redundant, and irrelevant. By incorporating feature subcategorization, the method simultaneously satisfies the requirements for both minimal-optimal features and all-relevant features, which are crucial for real-world applications. The experiments used synthetic data with predefined feature categories and real-world data. Evaluation was conducted based on two metrics: the first measured the percentage of relevant features selected, and the second assessed whether the importance level of each feature was accurately determined. Based on the experimental findings, the proposed method demonstrated greater precision in assessing the feature ranks and facilitated the creation of a more accurate learning model. Moreover, it enhanced the interpretability and utility of the feature selection results. | |||||||
| 言語 | en | |||||||
| 内容記述 | ||||||||
| 内容記述タイプ | Other | |||||||
| 内容記述 | Version of record: 03 May 2025 | |||||||
| 言語 | en | |||||||
| 出版者 | ||||||||
| 出版者 | Springer | |||||||
| 言語 | en | |||||||
| 言語 | ||||||||
| 言語 | eng | |||||||
| 資源タイプ | ||||||||
| 資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||||
| タイプ | journal article | |||||||
| 出版タイプ | ||||||||
| 出版タイプ | AM | |||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||
| 関連情報 | ||||||||
| 関連タイプ | isVersionOf | |||||||
| 識別子タイプ | DOI | |||||||
| 関連識別子 | https://doi.org/10.1007/s42979-025-03982-7 | |||||||
| 収録物識別子 | ||||||||
| 収録物識別子タイプ | EISSN | |||||||
| 収録物識別子 | 2661-8907 | |||||||
| 書誌情報 |
en : SN Computer Science 巻 6, p. 442, 発行日 2025-05-03 |
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| ファイル公開日 | ||||||||
| 日付 | 2026-05-03 | |||||||
| 日付タイプ | Available | |||||||
