  This paper deals with a new filter algorithm for selecting the smallest
subset of features carrying all the information content of a data set (i.e. for
removing redundant features). It is an advanced version of the fractal
dimension reduction technique, and it relies on the recently introduced
Morisita estimator of Intrinsic Dimension (ID). Here, the ID is used to
quantify dependencies between subsets of features, which allows the effective
processing of highly non-linear data. The proposed algorithm is successfully
tested on simulated and real world case studies. Different levels of sample
size and noise are examined along with the variability of the results. In
addition, a comprehensive procedure based on random forests shows that the data
dimensionality is significantly reduced by the algorithm without loss of
relevant information. And finally, comparisons with benchmark feature selection
techniques demonstrate the promising performance of this new filter.
