FASTENER Feature Selection for Inference from Earth Observation Data

Abstract

In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set 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. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario.

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“…The feature selection process can be achieved using a more thorough search through the grid of features. Greedy algorithms would not be able to accomplish this task in a reasonable time; however, smart heuristics powered by genetic programming and entropy-based similarity measures can canvas the most relevant sections of the feature space and extract (almost) optimal feature vectors from a large feature space, usually further improving accuracy of a particular model [39].…”

“…feature space and extract (almost) optimal feature vectors from a large feature space, usually further improving accuracy of a particular model [39].…”