145 citations · 793 across the 36 of their papers we have counts for
6 papers · 1 filter
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Joseph D. Romano, Trang T. Le, William La Cava +7
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets. Few tools exist that…
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments
Ryan J. Urbanowicz, Pranshu Suri, Yuhan Cui +4
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 ass…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
Is deep learning necessary for simple classification tasks?
Joseph D. Romano, Trang T. Le, Weixuan Fu +1
Automated machine learning (AutoML) and deep learning (DL) are two cutting-edge paradigms used to solve a myriad of inductive learning tasks. In spite of their successes, little gu…
Genetic programming approaches to learning fair classifiers
William La Cava, Jason H. Moore
Society has come to rely on algorithms like classifiers for important decision making, giving rise to the need for ethical guarantees such as fairness. Fairness is typically define…
SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
Stefano Ruberto, Valerio Terragni, Jason H. Moore
Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) tha…