1 code implementation • 18 Jan 2018 • Pieter Gijsbers, Joaquin Vanschoren, Randal S. Olson
With the demand for machine learning increasing, so does the demand for tools which make it easier to use.
2 code implementations • 22 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.
no code implementations • 22 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.
no code implementations • 9 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.
2 code implementations • 17 Sep 2017 • Arend Hintze, Jeffrey A. Edlund, Randal S. Olson, David B. Knoester, Jory Schossau, Larissa Albantakis, Ali Tehrani-Saleh, Peter Kvam, Leigh Sheneman, Heather Goldsby, Clifford Bohm, Christoph Adami
Markov Brains are a class of evolvable artificial neural networks (ANN).
2 code implementations • 8 Aug 2017 • Randal S. Olson, William La Cava, Zairah Mustahsan, Akshay Varik, Jason H. Moore
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms.
1 code implementation • 1 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.
1 code implementation • 1 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.
no code implementations • 6 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.
1 code implementation • 29 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.
no code implementations • 27 Mar 2016 • Randal S. Olson, Jason H. Moore, Christoph Adami
Pattern recognition and classification is a central concern for modern information processing systems.
3 code implementations • 20 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.
no code implementations • 29 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."
1 code implementation • 28 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.
no code implementations • 8 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.
no code implementations • 23 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.
7 code implementations • 22 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.
1 code implementation • 14 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.