Feature and Instance Joint Selection: A Reinforcement Learning Perspective
This work addresses a data processing challenge for machine learning practitioners, but it is incremental as it builds on existing joint selection methods.
The paper tackles the problem of jointly selecting features and instances by capturing fine-grained interactions between them, proposing a reinforcement learning solution that demonstrates improved performance on real-world datasets.
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.