强化学习
计算机科学
特征选择
选择(遗传算法)
人工智能
机器学习
特征(语言学)
接头(建筑物)
任务(项目管理)
透视图(图形)
动作选择
作者
Fan, Wei,Liu, Kunpeng,Liu, Hao,Zhu, Hengshu,Xiong, Hui,Fu, Yanjie
标识
DOI:10.24963/ijcai.2022/277
摘要
Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance selection coarsely; thus neglecting the latent fine-grained interaction between feature space and instance space. To address this challenge, we propose a reinforcement learning solution to accomplish the joint selection task and simultaneously capture the interaction between the selection of each feature and each instance. In particular, a sequential-scanning mechanism is designed as action strategy of agents, and a collaborative-changing environment is used to enhance agent collaboration. In addition, an interactive paradigm introduces prior selection knowledge to help agents for more efficient exploration. Finally, extensive experiments on real-world datasets have demonstrated improved performances.
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