Nature inspired feature selection meta-heuristics

Many strategies have been exploited for the task of feature selection, in an effort to identify more compact and better quality feature subsets. A number of evaluation metrics have been developed recently that can judge the quality of a given feature subset as a whole, rather than assessing the qualities of individual features. Effective techniques of stochastic nature have also emerged, allowing good quality solutions to be discovered without resorting to exhaustive search. This paper provides a comprehensive review of the most recent methods for feature selection that originated from nature inspired meta-heuristics, where the more classic approaches such as genetic algorithms and ant colony optimisation are also included for comparison. A good number of the reviewed methodologies have been significantly modified in the present, in order to systematically support generic subset-based evaluators and higher dimensional problems. Such modifications are carried out because the original studies either work exclusively with certain subset evaluators (e.g., rough set-based methods), or are limited to specific problem domains. A total of ten different algorithms are examined, and their mechanisms and work flows are summarised in an unified manner. The performance of the reviewed approaches are compared using high dimensional, real-valued benchmark data sets. The selected feature subsets are also used to build classification models, in an effort to further validate their efficacies.