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Found 39 papers, 26 papers with code
Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning
In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution.
Benchmarking AutoML algorithms on a collection of synthetic classification problems
Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets.
Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems
The discussed modifications are evaluated based on a number of difficult continuous-optimization benchmarks.
Automatically Balancing Model Accuracy and Complexity using Solution and Fitness Evolution (SAFE)
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
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
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.
Contemporary Symbolic Regression Methods and their Relative Performance
We assess 14 symbolic regression methods and 7 machine learning methods on a set of 252 diverse regression problems.
Benchmarking AutoML Frameworks for Disease Prediction Using Medical Claims
We ascertain and compare the performances of AutoML tools on large, highly imbalanced healthcare datasets.
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application.
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics.
PMLB v1.0: An open source dataset collection for benchmarking machine learning methods
Motivation: Novel machine learning and statistical modeling studies rely on standardized comparisons to existing methods using well-studied benchmark datasets.
A Rigorous Machine Learning Analysis Pipeline for Biomedical Binary Classification: Application in Pancreatic Cancer Nested Case-control Studies with Implications for Bias Assessments
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 associations.
Benchmarking in Optimization: Best Practice and Open Issues
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world.
Is deep learning necessary for simple classification tasks?
providing a head-to-head comparison of AutoML and DL in the context of binary classification on 6 well-characterized public datasets, and (2.)
Genetic programming approaches to learning fair classifiers
In this paper, current approaches to fairness are discussed and used to motivate algorithmic proposals that incorporate fairness into genetic programming for classification.
SGP-DT: Semantic Genetic Programming Based on Dynamic Targets
The evolution in each run is guided by a new (dynamic) target based on the residual errors.
Recommendations to enhance rigor and reproducibility in biomedical research
Computational methods have reshaped the landscape of modern biology.
Evaluating recommender systems for AI-driven biomedical informatics
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.
Semantic variation operators for multidimensional genetic programming
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
Electronic health records are an increasingly important resource for understanding the interactions between patient health, environment, and clinical decisions.
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-generation biclustering algorithm for mining genetic data.
Learning concise representations for regression by evolving networks of trees
We propose and study a method for learning interpretable representations for the task of regression.
Where are we now? A large benchmark study of recent symbolic regression methods
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.
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced.
Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining
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.
Relief-Based Feature Selection: Introduction and Review
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.
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure
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.
A probabilistic and multi-objective analysis of lexicase selection and epsilon-lexicase selection
Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection.
Data-driven Advice for Applying Machine Learning to Bioinformatics Problems
As the bioinformatics field grows, it must keep pace not only with new data but with new algorithms.
Investigating the Parameter Space of Evolutionary Algorithms
The practice of evolutionary algorithms involves the tuning of many parameters.
A System for Accessible Artificial Intelligence
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.
Ensemble representation learning: an analysis of fitness and survival for wrapper-based genetic programming methods
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.
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
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.
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 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.
Identifying and Harnessing the Building Blocks of Machine Learning Pipelines for Sensible Initialization of a Data Science Automation Tool
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.
Evolution of active categorical image classification via saccadic eye movement
Pattern recognition and classification is a central concern for modern information processing systems.
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-increasing demand for tools that make machine learning accessible to non-experts.
Exploring the coevolution of predator and prey morphology and behavior
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
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.
