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Recently we proposed a general, ensemble-based feature engineering wrapper(FEW) that was paired with a number of machine learning methods to solveregression problems. Here, we adapt FEW for supervised classification andperform a thorough analysis of fitness and survival methods within thisframework. Our tests demonstrate that two fitness metrics, one introduced as anadaptation of the silhouette score, outperform the more commonly used Fishercriterion. We analyze survival methods and demonstrate that $\epsilon$-lexicasesurvival works best across our test problems, followed by random survival whichoutperforms both tournament and deterministic crowding. We conduct a benchmarkcomparison to several classification methods using a large set of problems andshow that FEW can improve the best classifier performance in several cases. Weshow that FEW generates consistent, meaningful features for a biomedicalproblem with different ML pairings.

Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods