Published April 17, 2026
| Version v1.0.0
Dataset
Open
Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification
Authors/Creators
Description
This repository is organized into two main directories: DATA/ and Results/.
DATA/
Contains the input datasets. These files include the structured configuration–performance tables required to perform the functional ANOVA (fANOVA) analysis.results/
Contains all outputs generated from the experiments. This directory is further divided into two subdirectories:EXP1/
Includes the results of the first experiment, where fANOVA was applied to analyze the contribution of different pipeline components. This folder contains:- Raw fANOVA output files (e.g., variance decompositions and importance scores)
- Generated visualizations such as bar plots, heatmaps, and other figures summarizing main and interaction effects
EXP2/
Contains the corresponding outputs for the second experiment, following the same structure asEXP1/. It includes:- fANOVA result files
- Visualization artifacts illustrating the importance of components and their interactions
Files
DATA.zip
Files
(683.5 kB)
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md5:9cbb99af4d001e23cdacb3ddb964ffec
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3.9 kB | Preview Download |
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md5:4731eb8e20935e359b69087a0ab57df6
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Additional details
Funding
- European Commission
- AutoLearn-SI - Leveraging Benchmarking Data for Automated Machine Learning and Optimization 101187010
- The Slovenian Research and Innovation Agency
- Knowledge technologies P2-0103
- The Slovenian Research and Innovation Agency
- From Big Data to Good Data: Smart-sized Benchmarking for Trustworthy Artificial Intelligence J2-70078
- The Slovenian Research and Innovation Agency
- AI for Science GC-0001
- The Slovenian Research and Innovation Agency
- Computer systems and structures P2-0098
Dates
- Available
-
2026
Software
- Repository URL
- https://github.com/AutoLearnSI/Design-Choices-That-Matter-A-Functional-ANOVA-Analysis-for-Remote-Sensing-Multi-Label-Classification
- Programming language
- Python