145 citations · 793 across the 36 of their papers we have counts for
10 papers · 1 filter
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt +2
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) dete…
Relief-Based Feature Selection: Introduction and Review
Ryan J. Urbanowicz, Melissa Meeker, William LaCava +2
Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computational…
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure
Alena Orlenko, Jason H. Moore, Patryk Orzechowski +6
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinic…
A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
William La Cava, Thomas Helmuth, Lee Spector +1
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has de…
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
Randal S. Olson, William La Cava, Zairah Mustahsan +2
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms. Here we contribute a thorough analysis of 13 state-of-the-art, commonly used ma…
Investigating the Parameter Space of Evolutionary Algorithms
Moshe Sipper, Weixuan Fu, Karuna Ahuja +1
The practice of evolutionary algorithms involves the tuning of many parameters. How big should the population be? How many generations should the algorithm run? What is the (tourna…
A System for Accessible Artificial Intelligence
Randal S. Olson, Moshe Sipper, William La Cava +7
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 knowled…
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods
William La Cava, Jason H. Moore
Recently we proposed a general, ensemble-based feature engineering wrapper (FEW) that was paired with a number of machine learning methods to solve regression problems. Here, we ad…
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Randal S. Olson, William La Cava, Patryk Orzechowski +2
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. Numerous p…
Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming
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…