Search Results for author: Ryan J. Urbanowicz

Found 12 papers, 7 papers with code

Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)

no code implementations30 Jun 2022 Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e. g., attaining both high accuracy and low complexity (to promote interpretability).

Solution and Fitness Evolution (SAFE): A Study of Multiobjective Problems

no code implementations25 Jun 2022 Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions.

Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functions

no code implementations25 Jun 2022 Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{conflating} the objective with the objective function.


STREAMLINE: A Simple, Transparent, End-To-End Automated Machine Learning Pipeline Facilitating Data Analysis and Algorithm Comparison

1 code implementation23 Jun 2022 Ryan J. Urbanowicz, Robert Zhang, Yuhan Cui, Pranshu Suri

Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations.

Binary Classification Feature Importance +3

LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification

no code implementations26 Apr 2021 Robert Zhang, Rachael Stolzenberg-Solomon, Shannon M. Lynch, Ryan J. Urbanowicz

Machine learning (ML) research has yielded powerful tools for training accurate prediction models despite complex multivariate associations (e. g. interactions and heterogeneity).

BIG-bench Machine Learning Fairness +1

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

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

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

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

Cannot find the paper you are looking for? You can Submit a new open access paper.