Ryan J. Urbanowicz

发表

Learning classifier systems: introducing the user-friendly textbook

Will N. Browne, Ryan J. Urbanowicz, R. Urbanowicz, 2013, GECCO.

STatistical Inference Relief (STIR) feature selection

Jason H. Moore, Ryan J. Urbanowicz, Trang T. Le, 2018, bioRxiv.

The Application of Pittsburgh-Style Learning Classifier Systems to Address Genetic Heterogeneity and Epistasis in Association Studies

Jason H. Moore, Ryan J. Urbanowicz, R. Urbanowicz, 2010, PPSN.

Attribute tracking: strategies towards improved detection and characterization of complex associations

Jason H. Moore, Ryan J. Urbanowicz, John H. Holmes, 2018, GECCO.

The application of michigan-style learning classifiersystems to address genetic heterogeneity and epistasisin association studies

Jason H. Moore, Ryan J. Urbanowicz, R. Urbanowicz, 2010, GECCO '10.

Special issue on advances in Learning Classifier Systems

Ryan J. Urbanowicz, Kamran Shafi, Muhammad Iqbal, 2013, Evol. Intell..

An analysis pipeline with statistical and visualization-guided knowledge discovery for Michigan-style learning classifier systems

Jason H. Moore, Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, 2012, IEEE Computational Intelligence Magazine.

Statistical Inference Relief (STIR) feature selection

Jason H. Moore, Ryan J. Urbanowicz, Trang T. Le, 2018 .

Continuous Endpoint Data Mining with ExSTraCS: A Supervised Learning Classifier System

Jason H. Moore, Ryan J. Urbanowicz, Niranjan Ramanand, 2015, GECCO.

Introduction to Learning Classifier Systems

Will N. Browne, Ryan J. Urbanowicz, R. Urbanowicz, 2017, SpringerBriefs in Intelligent Systems.

A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments pdf

Ryan J. Urbanowicz, Yuchen Lu, Jason H. Moore, 2020, ArXiv.

Pareto Inspired Multi-objective Rule Fitness for Adaptive Rule-based Machine Learning

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2016, GECCO.

PMLB: a large benchmark suite for machine learning evaluation and comparison

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2017, BioData Mining.

Introducing Rule-Based Machine Learning: Capturing Complexity

Ryan J. Urbanowicz, R. Urbanowicz, 2016, GECCO.

Learning classifier systems: a complete introduction, review, and roadmap

Jason H. Moore, Ryan J. Urbanowicz, R. Urbanowicz, 2009 .

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

Jason H. Moore, Moshe Sipper, Ryan J. Urbanowicz, 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

Introducing rule-based machine learning: capturing complexity

Ryan J. Urbanowicz, 2017, GECCO '17.

Random artificial incorporation of noise in a learning classifier system environment

Jason H. Moore, Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, 2011, GECCO.

Automating Biomedical Data Science Through Tree-Based Pipeline Optimization pdf

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2016, EvoApplications.

Collective feature selection to identify crucial epistatic variants

Xinyuan Zhang, Jason H. Moore, Ryan J. Urbanowicz, 2018, BioData Mining.

Benchmarking Relief-Based Feature Selection Methods pdf

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2017, ArXiv.

A classification and characterization of two-locus, pure, strict, epistatic models for simulation and detection

Jason H. Moore, Ryan J. Urbanowicz, Jeff Kiralis, 2014, BioData Mining.

Introducing Rule-based Machine Learning: A Practical Guide

Will N. Browne, Ryan J. Urbanowicz, R. Urbanowicz, 2015, GECCO.

Problem Driven Machine Learning by Co-evolving Genetic Programming Trees and Rules in a Learning Classifier System

Jason H. Moore, Ryan J. Urbanowicz, Ben Yang, 2017, GPTP.

The detection and characterization of epistasis and heterogeneity: a learning classifier system approach

Jason H. Moore, Ryan J. Urbanowicz, R. Urbanowicz, 2012 .

Rule-based machine learning classification and knowledge discovery for complex problems

Ryan J. Urbanowicz, R. Urbanowicz, 2015 .

Introducing learning classifier systems: rules that capture complexity

Ryan J. Urbanowicz, Danilo Vasconcellos Vargas, R. Urbanowicz, 2018, GECCO.

Mask functions for the symbolic modeling of epistasis using genetic programming

Jason H. Moore, Ryan J. Urbanowicz, Bill C. White, 2008, GECCO '08.

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

Jason H. Moore, Moshe Sipper, Ryan J. Urbanowicz, 2019, EuroGP.

A simple multi-core parallelization strategy for learning classifier system evaluation

Jason H. Moore, Ryan J. Urbanowicz, James Rudd, 2013, GECCO.

Relief-Based Feature Selection: Introduction and Review

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2017, J. Biomed. Informatics.

Retooling Fitness for Noisy Problems in a Supervised Michigan-style Learning Classifier System

Jason H. Moore, Ryan J. Urbanowicz, 2015, GECCO.

A multi-core parallelization strategy for statistical significance testing in learning classifier systems

Jason H. Moore, Ryan J. Urbanowicz, James Rudd, 2013, Evolutionary Intelligence.

Functional Cycle Components

Ryan J. Urbanowicz, Will N. Browne, 2017 .

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2016, GECCO.

Rapid Rule Compaction Strategies for Global Knowledge Discovery in a Supervised Learning Classifier System

Jason H. Moore, Ryan J. Urbanowicz, Jie Tan, 2013, ECAL.

Learning Classifier Systems: The Rise of Genetics-Based Machine Learning in Biomedical Data Mining

Jason H. Moore, Ryan J. Urbanowicz, R. Urbanowicz, 2014 .

New Pathways in Coevolutionary Computation

Jason H. Moore, Moshe Sipper, Ryan J. Urbanowicz, 2019, GPTP.

Predicting the difficulty of pure, strict, epistatic models: metrics for simulated model selection

Jason H. Moore, Ryan J. Urbanowicz, Jeff Kiralis, 2012, BioData Mining.

Pareto Inspired Multi-objective Rule Fitness for Noise-Adaptive Rule-Based Machine Learning

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2016, PPSN.

A scikit-learn compatible learning classifier system

Ryan J. Urbanowicz, Robert F. Zhang, R. Urbanowicz, 2020, GECCO Companion.

Using Machine Learning on Home Health Care Assessments to Predict Fall Risk

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2019, MedInfo.

Special issue on the 20th anniversary of XCS

Tim Kovacs, Ryan J. Urbanowicz, Kamran Shafi, 2015, Evolutionary Intelligence.

Special issue on advances in learning classifier systems

Daniele Loiacono, Ryan J. Urbanowicz, Albert Orriols-Puig, 2012, Evol. Intell..

To know the objective is not (necessarily) to know the objective function

Jason H. Moore, Moshe Sipper, Ryan J. Urbanowicz, 2018, BioData Mining.

Instance-linked attribute tracking and feedback for michigan-style supervised learning classifier systems

Jason H. Moore, Ryan J. Urbanowicz, Ambrose Granizo-Mackenzie, 2012, GECCO '12.

ExSTraCS 2.0: description and evaluation of a scalable learning classifier system

Jason H. Moore, Ryan J. Urbanowicz, R. Urbanowicz, 2015, Evolutionary Intelligence.

Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach

Jason H. Moore, Ryan J. Urbanowicz, Angeline S. Andrew, 2013, J. Am. Medical Informatics Assoc..

Coevolving Artistic Images Using OMNIREP

Moshe Sipper, Ryan J. Urbanowicz, Jason H. Moore, 2020, EvoMUSART.

An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining

Jason H. Moore, Ryan J. Urbanowicz, Gediminas Bertasius, 2014, PPSN.

Using Expert Knowledge to Guide Covering and Mutation in a Michigan Style Learning Classifier System to Detect Epistasis and Heterogeneity

Jason H. Moore, Ryan J. Urbanowicz, Delaney Granizo-MacKenzie, 2012, PPSN.

GAMETES: a fast, direct algorithm for generating pure, strict, epistatic models with random architectures

Jason H. Moore, Ryan J. Urbanowicz, Nicholas A. Sinnott-Armstrong, 2012, BioData Mining.

Hands-on Workshop on Learning Classifier Systems

Will N. Browne, Ryan J. Urbanowicz, Karthik Kuber, 2016, GECCO.

Benchmarking Relief-Based Feature Selection Methods

Randal S. Olson, Jason H. Moore, Ryan J. Urbanowicz, 2017, J. Biomed. Informatics.

Evolutionary algorithms in biomedical data mining: challenges, solutions, and frontiers

Moshe Sipper, Ryan J. Urbanowicz, M. Sipper, 2020, GECCO Companion.

LCSs in a Nutshell

Ryan J. Urbanowicz, Will N. Browne, 2017 .

RARE: evolutionary feature engineering for rare-variant bin discovery

Ryan J. Urbanowicz, Satvik Dasariraju, R. Urbanowicz, 2021, GECCO Companion.

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

Ryan J. Urbanowicz, Robert F. Zhang, Robert Zhang, 2021, ArXiv.