  With the rising number of machine learning competitions, the world has
witnessed an exciting race for the best algorithms. However, the involved data
selection process may fundamentally suffer from evidence ambiguity and concept
drift issues, thereby possibly leading to deleterious effects on the
performance of various models. This paper proposes a new Reinforced Data
Sampling (RDS) method to learn how to sample data adequately on the search for
useful models and insights. We formulate the optimisation problem of model
diversification $\delta{-div}$ in data sampling to maximise learning potentials
and optimum allocation by injecting model diversity. This work advocates the
employment of diverse base learners as value functions such as neural networks,
decision trees, or logistic regressions to reinforce the selection process of
data subsets with multi-modal belief. We introduce different ensemble reward
mechanisms, including soft voting and stochastic choice to approximate optimal
sampling policy. The evaluation conducted on four datasets evidently highlights
the benefits of using RDS method over traditional sampling approaches. Our
experimental results suggest that the trainable sampling for model
diversification is useful for competition organisers, researchers, or even
starters to pursue full potentials of various machine learning tasks such as
classification and regression. The source code is available at
https://github.com/probeu/RDS.
