FASTENER Feature Selection for Inference from Earth Observation Data

Entropy

Klemen Kenda¹  Filip Koprivec¹  Beno Šircelj¹ 

[1] Jožef Stefan Institute, 1000 Ljubljana, Slovenia;

关键词: feature selection;    machine learning;    earth observation;    genetic algorithm;    information theory;   

DOI  :  10.3390/e22111198

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown