IJSDIP | International Journal of System Design & Info Processing

Optimal Feature Selection using Particle Swarm Optimization for Efficient Horse Race Prediction

Paper ID:

IJSDIP_2025_VOLUME 13_ISSUE 3_22-31

Author's Name:

Dr Meenakshi Gupta, Dr Latika Singh

Volume:

Issue:

Issue 3

Year:

2025

Pages:

22-31

Abstract

This paper investigates the application of Particle Swarm Optimization in feature selection to improve model accuracy for predicting horse racing results. PSO is a nature-inspired optimization technique miming evolution and animal behavior, effectively searching vast solution spaces for the best features. This methodology applies PSO to a horse racing dataset, proving its accuracy and computational time performance better than traditional filter and wrapper methods. A considerable improvement in the prediction accuracy and AUC-ROC values for the critical machine learning classifiers Random Forest, k-NN, and Logistic Regression is observed when these classifiers are integrated with PSO-based feature selection. The results would confirm the potential of PSO for making the feature selection process highly fluid and leading to robust real-time applications by the stakeholders in horse racing, with improvements in the decision-making process and results from betting actions, thereby empowering the audience with a sense of control and informed decision-making.

Keywords

Horse race prediction, Particle swarm optimization, feature selection, Random Forest, k-NN, Logistic Regression

References

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