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Found 16 papers, 6 papers with code
In-Domain Self-Supervised Learning Improves Remote Sensing Image Scene Classification
A common approach in practice to SSL pre-training is utilizing standard pre-training datasets, such as ImageNet.
FAIRification of MLC data
The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing number of papers and methods that appear in the literature.
Explainable Model-specific Algorithm Selection for Multi-Label Classification
To address this algorithm selection problem, we investigate in this work the quality of an automated approach that uses characteristics of the datasets - so-called features - and a trained algorithm selector to choose which algorithm to apply for a given task.
Discover the Mysteries of the Maya: Selected Contributions from the Machine Learning Challenge & The Discovery Challenge Workshop at ECML PKDD 2021
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021).
Semi-supervised Predictive Clustering Trees for (Hierarchical) Multi-label Classification
Semi-supervised learning (SSL) is a common approach to learning predictive models using not only labeled examples, but also unlabeled examples.
Current Trends in Deep Learning for Earth Observation: An Open-source Benchmark Arena for Image Classification
We present AiTLAS: Benchmark Arena -- an open-source benchmark suite for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO).
AiTLAS: Artificial Intelligence Toolbox for Earth Observation
The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as repository of AI-ready Earth Observation (EO) datasets.
Discovering outliers in the Mars Express thermal power consumption patterns
The Mars Express (MEX) spacecraft has been orbiting Mars since 2004.
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data.
Explaining the Performance of Multi-label Classification Methods with Data Set Properties
Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data.
Comprehensive Comparative Study of Multi-Label Classification Methods
Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods.
Ensemble- and Distance-Based Feature Ranking for Unsupervised Learning
In this work, we propose two novel (groups of) methods for unsupervised feature ranking and selection.
Feature Ranking for Semi-supervised Learning
This poses a variety of challenges for the existing machine learning methods: coping with dataset with a large number of examples that are described in a high-dimensional space and not all examples have labels provided.
Fuzzy Jaccard Index: A robust comparison of ordered lists
In real-life, and in particular high-dimensional domains, where only a small percentage of the whole feature space might be relevant, a robust and confident feature ranking leads to interpretable findings as well as efficient computation and good predictive performance.
Oblique Predictive Clustering Trees
Also, learning of PCTs can not exploit the sparsity of data to improve the computational efficiency, which is common in both input (molecular fingerprints, bag of words representations) and output spaces (in multi-label classification, examples are often labeled with only a fraction of possible labels).
Machine learning for predicting thermal power consumption of the Mars Express Spacecraft
The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range.
