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In wood construction, fasteners are essential for securing components together, with the connection strength largely dependent on the direct bearing between members. Various types of fasteners are employed, each suited to specific applications and structural requirements.
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Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
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Streaming Data Fusion for the Internet of Things.

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Related Experiment Video

Updated: Nov 27, 2025

Insertion of Flexible Neural Probes Using Rigid Stiffeners Attached with Biodissolvable Adhesive
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FASTENER Feature Selection for Inference from Earth Observation Data.

Filip Koprivec1,2,3, Klemen Kenda1,4, Beno Šircelj1

  • 1Jožef Stefan Institute, 1000 Ljubljana, Slovenia.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

A new feature selection algorithm, FASTENER, efficiently identifies optimal feature subsets for machine learning models. It enhances classification accuracy, especially in earth observation, by minimizing features while maximizing predictive power.

Keywords:
earth observationfeature selectiongenetic algorithminformation theorymachine learning

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Area of Science:

  • Machine Learning
  • Data Science
  • Remote Sensing

Background:

  • High-dimensional data presents challenges for machine learning model accuracy and efficiency.
  • Feature selection is crucial for optimizing model performance and reducing computational costs.
  • Existing methods may not effectively balance feature reduction with classification accuracy.

Purpose of the Study:

  • Introduce FASTENER, a novel multi-objective feature selection algorithm.
  • To maximize machine learning model accuracy using a minimal set of features.
  • To improve upon existing feature selection techniques in terms of speed and performance.

Main Methods:

  • FASTENER employs a multi-objective approach within an iterative genetic algorithm framework.
  • It utilizes entropy-based measures, including mutual information, during the crossover phase.
  • The algorithm was evaluated on Sentinel-2 land-cover classification data and open datasets.

Main Results:

  • FASTENER demonstrates faster convergence to optimal feature subsets compared to POSS, DT-forward, and FS-SDS.
  • It achieves superior classification accuracy over current similarity and information theory-based methods in earth observation.
  • The algorithm yields state-of-the-art results for land-cover classification and comparable/superior results on other datasets.

Conclusions:

  • FASTENER offers an efficient and effective solution for feature selection in high-dimensional data.
  • The algorithm provides state-of-the-art performance, particularly in remote sensing applications.
  • FASTENER is broadly applicable to any supervised machine learning task.