PREDICTION OF TRAFFIC ACCIDENT'S DATASET THROUGH SOFT-COMPUTING.

ABSTRACT

Feature selection is a critical step in the development of machine learning models, as it helps to improve model performance, reduce complexity, and enhance interpretability. This paper presents a comprehensive implementation of feature selection using a combined Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) approach. The proposed method leverages the optimization capabilities of PSO to identify the most relevant subset of features that maximize the predictive accuracy of the ANN. The process begins with the initialization of a swarm of particles, where each particle represents a potential feature subset. An ANN model is then trained using the selected features of each particle, and its performance is evaluated on a validation set. The fitness of each particle is determined by the performance metric of the ANN, guiding the swarm towards better solutions through iterative updates of particle positions and velocities. The final feature subset identified by PSO is used to train an ANN model, which is subsequently evaluated on a test set to assess its performance. This approach effectively reduces dimensionality, improves computational efficiency, and enhances the model’s generalization ability by removing irrelevant or redundant features. The implementation details, including data loading and preprocessing, ANN model creation, and PSO optimization, are provided to demonstrate the practical application of this method. The results indicate that the PSO-ANN algorithm is a powerful tool for feature selection, capable of improving the accuracy and efficiency of machine learning models.