Selective ensemble learning is a technique that selects a subset of diverse and accurate
basic models in order to generate stronger generalization ability. In this paper, we proposed
a novel learning algorithm that is based on parallel optimization and hierarchical selection
(PTHS). Our novel feature selection method is based on maximize the sum of relevance and
distance (MSRD) for solving the problem of high dimensionality. Specifically, we have a
PTHS algorithm that employs parallel optimization and candidate model pruning based on k …