Enhanced Predictive Modeling for Hazardous Near-Earth Object Detection: A Comparative Analysis of Advanced Resampling Strategies and Machine Learning Algorithms in Planetary Risk Assessment

TL;DR

This paper compares various machine learning models and resampling strategies for predicting hazardous near-Earth objects, finding ensemble methods like Random Forest and Gradient Boosting to be most effective with high accuracy and low false positives.

Contribution

It provides a comprehensive evaluation of multiple classifiers and resampling techniques for NEO hazard prediction, highlighting the superiority of ensemble methods in this context.

Findings

RFC and GBC achieved F2-scores above 0.98.

Ensemble methods showed the highest accuracy and lowest false positives.

KNN performed poorly due to complex data patterns.

Abstract

This study evaluates the performance of several machine learning models for predicting hazardous near-Earth objects (NEOs) through a binary classification framework, including data scaling, power transformation, and cross-validation. Six classifiers were compared, namely Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and K-Nearest Neighbors (KNN). RFC and GBC performed the best, both with an impressive F2-score of 0.987 and 0.986, respectively, with very small variability. SVC followed, with a lower but reasonable score of 0.896. LDA and LR had a moderate performance with scores of around 0.749 and 0.748, respectively, while KNN had a poor performance with a score of 0.691 due to difficulty in handling complex data patterns. RFC and GBC also presented great confusion matrices with a negligible number of false positives and false negatives, which resulted in outstanding accuracy rates of 99.7% and 99.6%, respectively. These findings highlight the power of ensemble methods for high precision and recall and further point out the importance of tailored model selection with regard to dataset characteristics and chosen evaluation metrics. Future research could focus on the optimization of hyperparameters with advanced features engineering to further the accuracy and robustness of the model on NEO hazard predictions.