Search Results for author: Randal S. Olson

Found 18 papers, 11 papers with code

Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining

2 code implementations22 Nov 2017 Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore

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.

Benchmarking feature selection

Relief-Based Feature Selection: Introduction and Review

no code implementations22 Nov 2017 Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore

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.

feature selection

Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure

no code implementations9 Oct 2017 Alena Orlenko, Jason H. Moore, Patryk Orzechowski, Randal S. Olson, Junmei Cairns, Pedro J. Caraballo, Richard M. Weinshilboum, Liewei Wang, Matthew K. Breitenstein

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.

AutoML BIG-bench Machine Learning +1

A System for Accessible Artificial Intelligence

1 code implementation1 May 2017 Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore

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.

BIG-bench Machine Learning

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

1 code implementation1 Mar 2017 Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore

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.

Benchmarking BIG-bench Machine Learning

Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming

no code implementations6 Feb 2017 Andrew Sohn, Randal S. Olson, Jason H. Moore

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.

BIG-bench Machine Learning Dimensionality Reduction

Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

1 code implementation29 Jul 2016 Randal S. Olson, Jason H. Moore

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.

AutoML BIG-bench Machine Learning +2

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

3 code implementations20 Mar 2016 Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore

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.

Automated Feature Engineering BIG-bench Machine Learning +2

Exploring the coevolution of predator and prey morphology and behavior

no code implementations29 Feb 2016 Randal S. Olson, Arend Hintze, Fred C. Dyer, Jason H. Moore, Christoph Adami

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

1 code implementation28 Jan 2016 Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore

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.

BIG-bench Machine Learning General Classification +1

Exploring the evolution of a trade-off between vigilance and foraging in group-living organisms

no code implementations8 Aug 2014 Randal S. Olson, Patrick B. Haley, Fred C. Dyer, Christoph Adami

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

no code implementations23 Oct 2013 Arend Hintze, Randal S. Olson, Christoph Adami, Ralph Hertwig

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

7 code implementations22 Oct 2013 Randal S. Olson, David B. Knoester, Christoph Adami

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

1 code implementation14 Sep 2012 Randal S. Olson, Arend Hintze, Fred C. Dyer, David B. Knoester, Christoph Adami

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.

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