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Found 12 papers, 9 papers with code
Benchmarking AutoML algorithms on a collection of synthetic classification problems
Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets.
Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems
The discussed modifications are evaluated based on a number of difficult continuous-optimization benchmarks.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application.
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics.
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
EBIC: an open source software for high-dimensional and big data biclustering analyses
Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data.
Where are we now? A large benchmark study of recent symbolic regression methods
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches.
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced.
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure
Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated.
A System for Accessible Artificial Intelligence
While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them.
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study.
