| 研究課題/領域番号 |
22K12144
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| 研究機関 | 筑波大学 |
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研究代表者 |
叶 秀彩 筑波大学, システム情報系, 助教 (60814001)
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| 研究期間 (年度) |
2022-04-01 – 2026-03-31
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| キーワード | feature selection / active feature selection |
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| 研究実績の概要 |
In this year, we mainly address the active feature selection-based method and application. Most existing feature selection methods focus on statically selecting the same informative features for each subtype and fail to consider the heterogeneity of samples which causes pattern differences in each subtype. We consider active feature selection to dynamically acquire different features in each subtype by combining the subtype classifier with the reinforcement learning (RL) agent in a cost-sensitive manner. We apply active feature selection for gene signature identification in renal cell carcinoma, which can select different gene signatures for different renal cell carcinoma subtypes.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
To explore different informative feature subsets for each subtype, we propose a novel active feature selection method and apply it to gene signature identification in renal cell carcinoma. By combining the subtype classifier with the reinforcement learning (RL) agent, our method can sequentially select the active features in each sample in a cost-sensitive manner. The application for gene signature identification in renal cell carcinoma show that our method can select different gene signatures for different renal cell carcinoma subtypes.
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| 今後の研究の推進方策 |
Next step, we will further consider distributed datasets. We will propose distributed collaborative feature learning methods and consider privacy preserving. Intermediate representation based methods and federated learning will be applied to design the distributed feature selection framework for data collaboration. We also consider to apply our method to distributed multi-view datasets for multi-view data collaboration.
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| 次年度使用額が生じた理由 |
Due to the COVID-19 pandemic, the international conference that I was planning to attend has been canceled.
I plan to join the international conference and collect the information about the latest research on feature learning and distributed data analysis.
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