  Feature subset selection (FSS) for classification is inherently a
bi-objective optimization problem, where the task is to obtain a feature subset
which yields the maximum possible area under the receiver operator
characteristic curve (AUC) with minimum cardinality of the feature subset. In
todays world, a humungous amount of data is generated in all activities of
humans. To mine such voluminous data, which is often high-dimensional, there is
a need to develop parallel and scalable frameworks. In the first-of-its-kind
study, we propose and develop an iterative MapReduce-based framework for
bi-objective evolutionary algorithms (EAs) based wrappers under Apache spark
with the migration strategy. In order to accomplish this, we parallelized the
non-dominated sorting based algorithms namely non dominated sorting algorithm
(NSGA-II), and non-dominated sorting particle swarm optimization (NSPSO), also
the decomposition-based algorithm, namely the multi-objective evolutionary
algorithm based on decomposition (MOEA-D), and named them P-NSGA-II-IS,
P-NSPSO-IS, P-MOEA-D-IS, respectively. We proposed a modified MOEA-D by
incorporating the non-dominated sorting principle while parallelizing it.
Throughout the study, AUC is computed by logistic regression (LR). We test the
effectiveness of the proposed methodology on various datasets. It is noteworthy
that the P-NSGA-II turns out to be statistically significant by being in the
top 2 positions on most datasets. We also reported the empirical attainment
plots, speed up analysis, and mean AUC obtained by the most repeated feature
subset and the least cardinal feature subset with the highest AUC, and
diversity analysis using hypervolume.
