Jason H. Moore

research

∙ 02/01/2023

Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning

In many evolutionary computation systems, parent selection methods can a...

0 Nicholas Matsumoto, et al. ∙

research

∙ 12/06/2022

Benchmarking AutoML algorithms on a collection of binary problems

Automated machine learning (AutoML) algorithms have grown in popularity ...

0 Pedro Henrique Ribeiro, et al. ∙

research

∙ 10/24/2022

Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems

Evolutionary multi-agent systems (EMASs) are very good at dealing with d...

0 Mateusz Godzik, et al. ∙

research

∙ 06/30/2022

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

When seeking a predictive model in biomedical data, one often has more t...

29 Moshe Sipper, et al. ∙

research

∙ 06/25/2022

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

We have recently presented SAFE – Solution And Fitness Evolution – a com...

14 Moshe Sipper, et al. ∙

research

∙ 06/25/2022

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

We recently highlighted a fundamental problem recognized to confound alg...

16 Moshe Sipper, et al. ∙

research

∙ 06/24/2022

Symbolic-Regression Boosting

Modifying standard gradient boosting by replacing the embedded weak lear...

10 Moshe Sipper, et al. ∙

research

∙ 07/29/2021

Contemporary Symbolic Regression Methods and their Relative Performance

Many promising approaches to symbolic regression have been presented in ...

39 William La Cava, et al. ∙

research

∙ 07/22/2021

Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims

We ascertain and compare the performances of AutoML tools on large, high...

23 Roland Albert A. Romero, et al. ∙

research

∙ 07/14/2021

Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers

Understanding the strengths and weaknesses of machine learning (ML) algo...

50 Patryk Orzechowski, et al. ∙

research

∙ 05/03/2021

EBIC.JL – an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia

Biclustering is a data mining technique which searches for local pattern...

23 Paweł Renc, et al. ∙

research

∙ 11/30/2020

PMLB v1.0: An open source dataset collection for benchmarking machine learning methods

Motivation: Novel machine learning and statistical modeling studies rely...

48 Joseph D. Romano, et al. ∙

research

∙ 08/28/2020

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

Machine learning (ML) offers a collection of powerful approaches for det...

20 Ryan J. Urbanowicz, et al. ∙

research

∙ 07/07/2020

Benchmarking in Optimization: Best Practice and Open Issues

This survey compiles ideas and recommendations from more than a dozen re...

11 Thomas Bartz-Beielstein, et al. ∙

research

∙ 06/11/2020

Is deep learning necessary for simple classification tasks?

Automated machine learning (AutoML) and deep learning (DL) are two cutti...

10 Joseph D. Romano, et al. ∙

research

∙ 04/28/2020

Genetic programming approaches to learning fair classifiers

Society has come to rely on algorithms like classifiers for important de...

18 William La Cava, et al. ∙

research

∙ 01/30/2020

SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

Semantic GP is a promising approach that introduces semantic awareness d...

31 Stefano Ruberto, et al. ∙

research

∙ 05/22/2019

Evaluating recommender systems for AI-driven data science

We present a free and open-source platform to allow researchers to easil...

0 William La Cava, et al. ∙

research

∙ 04/18/2019

Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as s...

10 William La Cava, et al. ∙

research

∙ 03/14/2019

Interpretation of machine learning predictions for patient outcomes in electronic health records

Electronic health records are an increasingly important resource for und...

0 William La Cava, et al. ∙

research

∙ 01/30/2019

Electronic Health Record Phenotyping with Internally Assessable Performance (PhIAP) using Anchor-Positive and Unlabeled Patients

Building phenotype models using electronic health record (EHR) data conv...

0 Lingjiao Zhang, et al. ∙

research

∙ 07/26/2018

EBIC: an open source software for high-dimensional and big data biclustering analyses

Motivation: In this paper we present the latest release of EBIC, a next-...

2 Patryk Orzechowski, et al. ∙

research

∙ 07/03/2018

Stochastic optimization approaches to learning concise representations

We propose and study a method for learning interpretable features via st...

2 William La Cava, et al. ∙

research

∙ 07/03/2018

Learning concise representations for regression by evolving networks of trees

We propose and study a method for learning interpretable representations...

0 William La Cava, et al. ∙

research

∙ 06/07/2018

Gamorithm

Examining games from a fresh perspective we present the idea of game-ins...

0 Moshe Sipper, et al. ∙

research

∙ 04/25/2018

Where are we now? A large benchmark study of recent symbolic regression methods

In this paper we provide a broad benchmarking of recent genetic programm...

0 Patryk Orzechowski, et al. ∙

research

∙ 01/09/2018

EBIC: an artificial intelligence-based parallel biclustering algorithm for pattern discovery

In this paper a novel biclustering algorithm based on artificial intelli...

0 Patryk Orzechowski, et al. ∙

research

∙ 11/22/2017

Relief-Based Feature Selection: Introduction and Review

Feature selection plays a critical role in data mining, driven by increa...

0 Ryan J. Urbanowicz, et al. ∙

research

∙ 10/09/2017

Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure

With the maturation of metabolomics science and proliferation of biobank...

0 Alena Orlenko, et al. ∙

research

∙ 09/15/2017

ε-Lexicase selection: a probabilistic and multi-objective analysis of lexicase selection in continuous domains

Lexicase selection is a parent selection method that considers training ...

0 William La Cava, et al. ∙

research

∙ 08/08/2017

Data-driven Advice for Applying Machine Learning to Bioinformatics Problems

As the bioinformatics field grows, it must keep pace not only with new d...

0 Randal S. Olson, et al. ∙

research

∙ 06/13/2017

Investigating the Parameter Space of Evolutionary Algorithms

The practice of evolutionary algorithms involves the tuning of many para...

0 Moshe Sipper, et al. ∙

research

∙ 05/01/2017

A System for Accessible Artificial Intelligence

While artificial intelligence (AI) has become widespread, many commercia...

0 Randal S. Olson, et al. ∙

research

∙ 03/20/2017

Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods

Recently we proposed a general, ensemble-based feature engineering wrapp...

0 William La Cava, et al. ∙

research

∙ 02/06/2017

Toward the automated analysis of complex diseases in genome-wide association studies using genetic programming

Machine learning has been gaining traction in recent years to meet the d...

0 Andrew Sohn, et al. ∙

research

∙ 07/29/2016

Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool

As data science continues to grow in popularity, there will be an increa...

0 Randal S. Olson, et al. ∙

research

∙ 03/27/2016

Evolution of active categorical image classification via saccadic eye movement

Pattern recognition and classification is a central concern for modern i...

0 Randal S. Olson, et al. ∙

research

∙ 03/20/2016

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-in...

0 Randal S. Olson, et al. ∙

research

∙ 02/29/2016

Exploring the coevolution of predator and prey morphology and behavior

A common idiom in biology education states, "Eyes in the front, the anim...

0 Randal S. Olson, et al. ∙

research

∙ 01/28/2016

Automating biomedical data science through tree-based pipeline optimization

Over the past decade, data science and machine learning has grown from a...

0 Randal S. Olson, et al. ∙

Jason H. Moore

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