  Recently we proposed a general, ensemble-based feature engineering wrapper
(FEW) that was paired with a number of machine learning methods to solve
regression problems. Here, we adapt FEW for supervised classification and
perform a thorough analysis of fitness and survival methods within this
framework. Our tests demonstrate that two fitness metrics, one introduced as an
adaptation of the silhouette score, outperform the more commonly used Fisher
criterion. We analyze survival methods and demonstrate that $\epsilon$-lexicase
survival works best across our test problems, followed by random survival which
outperforms both tournament and deterministic crowding. We conduct a benchmark
comparison to several classification methods using a large set of problems and
show that FEW can improve the best classifier performance in several cases. We
show that FEW generates consistent, meaningful features for a biomedical
problem with different ML pairings.
