データコラボレーション解析による分散協調特徴量選択手法の研究

本文主要研究基于主动特征选择的方法及其应用。现有的特征选择方法大多集中于静态地为每个亚型选择相同的信息特征，而没有考虑样本的异质性导致每个亚型的模式差异。我们考虑主动特征选择，通过将子类型分类器与强化学习（RL）代理以成本敏感的方式相结合，动态地获取每个子类型中的不同特征。我们将主动特征选择应用于肾癌的基因标签识别，可以为不同的肾癌亚型选择不同的基因标签。

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.