  Datasets with hundreds to tens of thousands features is the new norm. Feature
selection constitutes a central problem in machine learning, where the aim is
to derive a representative set of features from which to construct a
classification (or prediction) model for a specific task. Our experimental
study involves microarray gene expression datasets, these are high-dimensional
and noisy datasets that contain genetic data typically used for distinguishing
between benign or malicious tissues or classifying different types of cancer.
In this paper, we formulate feature selection as a multiagent coordination
problem and propose a novel feature selection method using multiagent
reinforcement learning. The central idea of the proposed approach is to
"assign" a reinforcement learning agent to each feature where each agent learns
to control a single feature, we refer to this approach as MARL. Applying this
to microarray datasets creates an enormous multiagent coordination problem
between thousands of learning agents. To address the scalability challenge we
apply a form of reward shaping called CLEAN rewards. We compare in total nine
feature selection methods, including state-of-the-art methods, and show that
the proposed method using CLEAN rewards can significantly scale-up, thus
outperforming the rest of learning-based methods. We further show that a hybrid
variant of MARL achieves the best overall performance.
