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Found 26 papers, 7 papers with code
Comparing Algorithm Selection Approaches on Black-Box Optimization Problems
Performance complementarity of solvers available to tackle black-box optimization problems gives rise to the important task of algorithm selection (AS).
Algorithm Instance Footprint: Separating Easily Solvable and Challenging Problem Instances
In black-box optimization, it is essential to understand why an algorithm instance works on a set of problem instances while failing on others and provide explanations of its behavior.
Artificial intelligence to advance Earth observation: a perspective
Earth observation (EO) is a prime instrument for monitoring land and ocean processes, studying the dynamics at work, and taking the pulse of our planet.
Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems
The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents.
Efficient Generator of Mathematical Expressions for Symbolic Regression
We empirically show that HVAE can be trained efficiently with small corpora of mathematical expressions and can accurately encode expressions into a smooth low-dimensional latent space.
Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms
In this work, we evaluate a performance prediction model built on top of the extension of the recently proposed OPTION ontology.
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.
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.
The Importance of Landscape Features for Performance Prediction of Modular CMA-ES Variants
In addition, we have shown that by using classifiers that take the features relevance on the model accuracy, we are able to predict the status of individual modules in the CMA-ES configurations.
Boosting the Performance of Quantum Annealers using Machine Learning
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution.
Unsupervised Feature Ranking via Attribute Networks
The need for learning from unlabeled data is increasing in contemporary machine learning.
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.
OPTION: OPTImization Algorithm Benchmarking ONtology
Many platforms for benchmarking optimization algorithms offer users the possibility of sharing their experimental data with the purpose of promoting reproducible and reusable research.
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.
ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings
The utility of ReliefE for high-dimensional data sets is ensured by its implementation that utilizes sparse matrix algebraic operations.
Multi-Label Classification
Vocal Bursts Intensity Prediction
Probabilistic Grammars for Equation Discovery
Equation discovery, also known as symbolic regression, is a type of automated modeling that discovers scientific laws, expressed in the form of equations, from observed data and expert knowledge.
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.
Feature Importance Estimation with Self-Attention Networks
Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret.
Equation Discovery for Nonlinear System Identification
In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies.
Meta-Model Framework for Surrogate-Based Parameter Estimation in Dynamical Systems
This task involves numerous evaluations of a computationally expensive objective function.
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.
Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients
However, in some instances, as with the attributes: testosterone, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis.
A framework for redescription set construction
In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences.
