  Feature subset selection (FSS) using a wrapper approach is essentially a
combinatorial optimization problem having two objective functions namely
cardinality of the selected-feature-subset, which should be minimized and the
corresponding area under the ROC curve (AUC) to be maximized. In this research
study, we propose a novel multiplicative single objective function involving
cardinality and AUC. The randomness involved in the Binary Differential
Evolution (BDE) may yield less diverse solutions thereby getting trapped in
local minima. Hence, we embed Logistic and Tent chaotic maps into the BDE and
named it as Chaotic Binary Differential Evolution (CBDE). Designing a scalable
solution to the FSS is critical when dealing with high-dimensional and
voluminous datasets. Hence, we propose a scalable island (iS) based
parallelization approach where the data is divided into multiple
partitions/islands thereby the solution evolves individually and gets combined
eventually in a migration strategy. The results empirically show that the
proposed parallel Chaotic Binary Differential Evolution (P-CBDE-iS) is able to
find the better quality feature subsets than the Parallel Bi-nary Differential
Evolution (P-BDE-iS). Logistic Regression (LR) is used as a classifier owing to
its simplicity and power. The speedup attained by the proposed parallel
approach signifies the importance.
