Jason H. Moore

DeepAI AI Chat

Featured Co-authors

Carola Doerr
90 publications
Markus Wagner
42 publications
Ying Jin
31 publications
Thomas Bartz-Beielstein
28 publications
Tome Eftimov
27 publications
Jakob Bossek
26 publications
Christoph Adami
25 publications
Yanyuan Ma
24 publications
Pascal Kerschke
23 publications
Moshe Sipper
23 publications
Marco Virgolin
19 publications

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. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world