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

在这一年里，我们主要研究了基于主动特征选择的方法和应用。现有的特征选择方法大多侧重于静态地为每个子类型选择相同的信息特征，而没有考虑样本的异质性，这导致了每个子类型的模式差异。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）代理以代价敏感的方式结合起来，动态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细胞癌的基因特征识别，可以针对不同的肾细胞癌亚型选择不同的基因特征。

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.