In this paper, FASTENER feature selection algorithm is presented. The algorithm exploits entropy-based measures such as mutual information in the crossover phase of the genetic algorithm approach. FASTENER converges to an (near) optimal subset of features faster than previous state-of-the-art algorithms and achieves better classification accuracy than similarity-based methods such as KBest or ReliefF or wrapper methods such as POSS. The approach was evaluated using the Earth Observation dataset for land-cover classification from ESA's Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. The algorithm can be used in any statistical learning scenario.
Submitted to Entropy
Filip Koprivec, Klemen Kenda, Beno Šircelj