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Integrated Evolutionary Learning: An Artificial Intelligence Approach to Joint Learning of Features and
Nina de Lacy1, Michael J Ramshaw1, J Nathan Kutz2
1de Lacy Laboratory, Department of Psychiatry, Huntsman Mental Health Institute, University of Utah, Salt Lake City, UT, United States.
Frontiers in Artificial Intelligence
|May 2, 2022
Summary
Integrated evolutionary learning (IEL) optimizes machine learning for complex health data by automating feature and hyperparameter selection. This approach enhances discovery science by providing explainable models and improving prediction accuracy.
Area of Science:
- Bioinformatics and Computational Biology
- Artificial Intelligence in Healthcare
- Machine Learning for Health Research
Background:
- Artificial intelligence (AI) and machine learning (ML) are increasingly used for analyzing large, multi-domain health datasets.
- Discovery science in unconstrained environments presents challenges, including numerous predictors and unknown hyperparameter ranges.
- Explainability is crucial for hypothesis generation and analysis in health research.
Purpose of the Study:
- To introduce Integrated Evolutionary Learning (IEL), a novel method for optimizing ML discovery science.
- To address challenges in large-scale health data analysis, including feature selection and hyperparameter tuning.
- To provide an automated, adaptive, and explainable approach for joint feature and hyperparameter learning.
Main Methods:
- Developed Integrated Evolutionary Learning (IEL), nesting ML algorithms within evolutionary algorithms.
- Utilized an information function for evolutionary selection of features and hyperparameters over generations.
- Applied IEL to deep learning (artificial neural networks), decision tree-based techniques, and linear models using heterogeneous biobehavioral data.
Main Results:
- IEL achieved ≥95% accuracy, sensitivity, and specificity with artificial neural networks in classification tasks.
- Achieved 45-73% R-squared (R²) in classification, demonstrating substantial gains over default ML settings.
- Demonstrated that IEL provides explainable models, retaining original features even with artificial neural networks.
Conclusions:
- IEL offers an automated, adaptive, and principled method for jointly learning features and hyperparameters in discovery science.
- The approach significantly expands the solution space and mitigates bias from manual tuning and feature selection.
- IEL provides flexibility in selecting the inner machine learning algorithm based on optimized learning outcomes for specific problems.
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