Search Results for author: Jason H. Moore

Found 39 papers, 26 papers with code

Benchmarking AutoML algorithms on a collection of synthetic classification problems

1 code implementation6 Dec 2022 Pedro Henrique Ribeiro, Patryk Orzechowski, Joost Wagenaar, Jason H. Moore

Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets.

AutoML Benchmarking

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

no code implementations30 Jun 2022 Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

When seeking a predictive model in biomedical data, one often has more than a single objective in mind, e. g., attaining both high accuracy and low complexity (to promote interpretability).

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

no code implementations25 Jun 2022 Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

We recently highlighted a fundamental problem recognized to confound algorithmic optimization, namely, \textit{conflating} the objective with the objective function.


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

no code implementations25 Jun 2022 Moshe Sipper, Jason H. Moore, Ryan J. Urbanowicz

We have recently presented SAFE -- Solution And Fitness Evolution -- a commensalistic coevolutionary algorithm that maintains two coevolving populations: a population of candidate solutions and a population of candidate objective functions.

Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers

1 code implementation14 Jul 2021 Patryk Orzechowski, Jason H. Moore

Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application.

Benchmarking BIG-bench Machine Learning

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

1 code implementation3 May 2021 Paweł Renc, Patryk Orzechowski, Aleksander Byrski, Jarosław Wąs, Jason H. Moore

Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics.

Is deep learning necessary for simple classification tasks?

2 code implementations11 Jun 2020 Joseph D. Romano, Trang T. Le, Weixuan Fu, Jason H. Moore

providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.)

AutoML Binary Classification +2

Genetic programming approaches to learning fair classifiers

2 code implementations28 Apr 2020 William La Cava, Jason H. Moore

In this paper, current approaches to fairness are discussed and used to motivate algorithmic proposals that incorporate fairness into genetic programming for classification.

Decision Making Fairness

SGP-DT: Semantic Genetic Programming Based on Dynamic Targets

no code implementations30 Jan 2020 Stefano Ruberto, Valerio Terragni, Jason H. Moore

The evolution in each run is guided by a new (dynamic) target based on the residual errors.

Evaluating recommender systems for AI-driven biomedical informatics

1 code implementation22 May 2019 William La Cava, Heather Williams, Weixuan Fu, Steve Vitale, Durga Srivatsan, Jason H. Moore

We have two goals in mind: first, to make it easy to construct sophisticated models of biomedical processes; and second, to provide a fully automated AI agent that can choose and conduct promising experiments for the user, based on the user's experiments as well as prior knowledge.

BIG-bench Machine Learning Collaborative Filtering +2

Semantic variation operators for multidimensional genetic programming

1 code implementation18 Apr 2019 William La Cava, Jason H. Moore

Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification.

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

1 code implementation14 Mar 2019 William La Cava, Christopher Bauer, Jason H. Moore, Sarah A Pendergrass

Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions.

BIG-bench Machine Learning Feature Importance

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

1 code implementation26 Jul 2018 Patryk Orzechowski, Jason H. Moore

Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data.

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

1 code implementation25 Apr 2018 Patryk Orzechowski, William La Cava, Jason H. Moore

In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches.

Benchmarking BIG-bench Machine Learning +2

EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover

1 code implementation9 Jan 2018 Patryk Orzechowski, Moshe Sipper, Xiuzhen Huang, Jason H. Moore

In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced.

Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining

2 code implementations22 Nov 2017 Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore

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) detect complex patterns of association (e. g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e. g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable.

Benchmarking feature selection

Relief-Based Feature Selection: Introduction and Review

no code implementations22 Nov 2017 Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore

Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations.

feature selection

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

no code implementations9 Oct 2017 Alena Orlenko, Jason H. Moore, Patryk Orzechowski, Randal S. Olson, Junmei Cairns, Pedro J. Caraballo, Richard M. Weinshilboum, Liewei Wang, Matthew K. Breitenstein

Automated Machine Learning (AutoML) approaches provide exciting opportunity to guide feature selection in agnostic metabolic profiling endeavors, where potentially thousands of independent data points must be evaluated.

AutoML BIG-bench Machine Learning +1

A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection

1 code implementation15 Sep 2017 William La Cava, Thomas Helmuth, Lee Spector, Jason H. Moore

Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection.

Program Synthesis regression +1

Investigating the Parameter Space of Evolutionary Algorithms

no code implementations13 Jun 2017 Moshe Sipper, Weixuan Fu, Karuna Ahuja, Jason H. Moore

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

A System for Accessible Artificial Intelligence

1 code implementation1 May 2017 Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore

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 knowledge of the systems required to use them.

BIG-bench Machine Learning

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

1 code implementation20 Mar 2017 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.

Feature Engineering General Classification +1

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

1 code implementation1 Mar 2017 Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, Jason H. Moore

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.

Benchmarking BIG-bench Machine Learning

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

no code implementations6 Feb 2017 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 in health care systems around the world.

BIG-bench Machine Learning Dimensionality Reduction

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

1 code implementation29 Jul 2016 Randal S. Olson, Jason H. Moore

In this chapter, we present a genetic programming-based AutoML system called TPOT that optimizes a series of feature preprocessors and machine learning models with the goal of maximizing classification accuracy on a supervised classification problem.

AutoML BIG-bench Machine Learning +2

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

3 code implementations20 Mar 2016 Randal S. Olson, Nathan Bartley, Ryan J. Urbanowicz, Jason H. Moore

As the field of data science continues to grow, there will be an ever-increasing demand for tools that make machine learning accessible to non-experts.

Automated Feature Engineering BIG-bench Machine Learning +2

Exploring the coevolution of predator and prey morphology and behavior

no code implementations29 Feb 2016 Randal S. Olson, Arend Hintze, Fred C. Dyer, Jason H. Moore, Christoph Adami

In this model, we observe a coevolutionary cycle between prey swarming behavior and the predator's visual system, where the predator and prey continually adapt their visual system and behavior, respectively, over evolutionary time in reaction to one another due to the well-known "predator confusion effect."

Automating biomedical data science through tree-based pipeline optimization

1 code implementation28 Jan 2016 Randal S. Olson, Ryan J. Urbanowicz, Peter C. Andrews, Nicole A. Lavender, La Creis Kidd, Jason H. Moore

Over the past decade, data science and machine learning has grown from a mysterious art form to a staple tool across a variety of fields in academia, business, and government.

BIG-bench Machine Learning General Classification +1

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