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Found 18 papers, 11 papers with code
Layered TPOT: Speeding up Tree-based Pipeline Optimization
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e. g. `omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e. g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e. g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable.
Relief-Based Feature Selection: Introduction and Review
Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations.
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.
Markov Brains: A Technical Introduction
Markov Brains are a class of evolvable artificial neural networks (ANN).
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms.
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.
Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming
Machine learning has been gaining traction in recent years to meet the demand for tools that can efficiently analyze and make sense of the ever-growing databases of biomedical data in health care systems around the world.
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem.
Evolution of active categorical image classification via saccadic eye movement
Pattern recognition and classification is a central concern for modern information processing systems.
Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science
As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.
Exploring the coevolution of predator and prey morphology and behavior
In this model, we observe a coevolutionary cycle between prey swarming behavior and the predator's visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known "predator confusion effect."
Automating biomedical data science through tree-based pipeline optimization
Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government.
Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms
Despite the fact that grouping behavior has been actively studied for over a century, the relative importance of the numerous proposed fitness benefits of grouping remain unclear.
Risk aversion as an evolutionary adaptation
We hypothesize that risk aversion in the equivalent mean payoff gamble is beneficial as an adaptation to living in small groups, and find that a preference for risk averse strategies only evolves in small populations of less than 1, 000 individuals, while agents exhibit no such strategy preference in larger populations.
Evolution of swarming behavior is shaped by how predators attack
Using an evolutionary model of a predator-prey system, we show that how predators attack is critical to the evolution of the selfish herd.
Predator confusion is sufficient to evolve swarming behavior
Using an evolutionary model of a predator-prey system, we show that predator confusion provides a sufficient selection pressure to evolve swarming behavior in prey.
