  Feature selection is an effective preprocessing technique to reduce data
dimension. For feature selection, rough set theory provides many measures,
among which mutual information is one of the most important attribute measures.
However, mutual information based importance measures are computationally
expensive and inaccurate, especially in hypersample instances, and it is
undoubtedly a NP-hard problem in high-dimensional hyperhigh-dimensional data
sets. Although many representative group intelligent algorithm feature
selection strategies have been proposed so far to improve the accuracy, there
is still a bottleneck when using these feature selection algorithms to process
high-dimensional large-scale data sets, which consumes a lot of performance and
is easy to select weakly correlated and redundant features. In this study, we
propose an incremental mutual information based improved swarm intelligent
optimization method (IMIICSO), which uses rough set theory to calculate the
importance of feature selection based on mutual information. This method
extracts decision table reduction knowledge to guide group algorithm global
search. By exploring the computation of mutual information of supersamples, we
can not only discard the useless features to speed up the internal and external
computation, but also effectively reduce the cardinality of the optimal feature
subset by using IMIICSO method, so that the cardinality is minimized by
comparison. The accuracy of feature subsets selected by the improved cockroach
swarm algorithm based on incremental mutual information is better or almost the
same as that of the original swarm intelligent optimization algorithm.
Experiments using 10 datasets derived from UCI, including large scale and high
dimensional datasets, confirmed the efficiency and effectiveness of the
proposed algorithm.
