  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.
