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Bioinspired Optimization For Feature Selection In Post-compliance Risk Prediction.

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Bioinspired Optimization For Feature Selection In Post-compliance Risk Prediction.

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Published on: October 11, 2018

Bioinspired Optimization for Feature Selection in Post-Compliance Risk Prediction.

Álex Paz^1,2, Broderick Crawford³, Eric Monfroy²

¹Escuela de Ingeniería en Construcción y Transporte, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2147, Valparaíso 2362804, Chile.

Biomimetics (Basel, Switzerland)

|March 27, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Bio-inspired metaheuristic optimization improves administrative risk prediction by selecting relevant features. This approach enhances minority-class recall and reduces feature dimensionality, particularly for imbalanced datasets.

Keywords:

bio-inspired optimization computational efficiency engineering optimization imbalanced classification metaheuristic algorithms swarm intelligence wrapper-based feature selection

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Area of Science:

Computational intelligence and machine learning
Data science and predictive analytics
Administrative science and public policy

Background:

Class imbalance and feature redundancy pose challenges in administrative risk prediction.
Conventional learning pipelines struggle with high-dimensional data and operational constraints.
Bio-inspired metaheuristic optimization offers flexible search mechanisms for complex predictive tasks.

Purpose of the Study:

To evaluate a wrapper-based metaheuristic feature selection framework for post-compliance income declaration prediction.
To integrate swarm-inspired optimization with supervised classifiers using a weighted objective function.
To jointly prioritize minority-class recall and subset compactness in predictive models.

Main Methods:

Utilized real longitudinal administrative records for income declaration prediction.
Implemented a wrapper-based metaheuristic feature selection framework.
Integrated swarm-inspired optimization with supervised classifiers (k-nearest neighbors, Random Forest, LightGBM).
Assessed robustness through 31 independent stochastic runs per configuration.

Main Results:

Metaheuristic feature selection significantly improved minority-class recall for variance-prone classifiers (e.g., k-nearest neighbors, Random Forest).
Optimized models for LightGBM maintained high recall with reduced feature dimensionality (16-33 features from 76).
Performance gains were learner-dependent, indicating the importance of classifier choice.
The approach demonstrated simultaneous control over minority-class performance and feature dimensionality.

Conclusions:

Metaheuristic-driven wrapper feature selection effectively reshapes predictive representations for imbalanced datasets.
The framework enables simultaneous optimization of minority-class performance and feature dimensionality.
Findings suggest potential for improved administrative risk prediction models.
Further investigation into institutional deployment and cross-domain generalization is warranted.