Search Results for author:
Found 15 papers, 9 papers with code
Coevolving Artistic Images Using OMNIREP
We have recently developed OMNIREP, a coevolutionary algorithm to discover both a representation and an interpreter that solve a particular problem of interest.
New Pathways in Coevolutionary Computation
The simultaneous evolution of two or more species with coupled fitness -- coevolution -- has been put to good use in the field of evolutionary computation.
STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers
While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines.
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
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
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
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
Machine learning (ML) offers powerful methods for detecting and modeling associations often in data with large feature spaces and complex associations.
LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification
Machine learning (ML) research has yielded powerful tools for training accurate prediction models despite complex multivariate associations (e. g. interactions and heterogeneity).
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments
Machine learning (ML) offers a collection of powerful approaches for detecting and modeling associations, often applied to data having a large number of features and/or complex associations.
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.
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.
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.
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.
