Performance comparison of feature selection and extraction methods with random instance selection

•We evaluate a framework for reducing run time of big data feature reduction methods.•This framework selects a small random subset of instances prior to feature reduction.•We present comprehensive computational experiments using large public datasets.•Execution time can be reduced by a factor of 90 with minimal impact on performance.•We provide recommendations on which feature reduction method to use with a classifier.