A novel hybrid model for species distribution prediction using probabilistic random forest, principal component analysis and genetic algorithm.

ABSTRACT: Probabilistic Random Forest is an extension of the traditional Random Forest machine learning algorithm that is one of the frequently used machine learning algorithms employed for species distribution modeling. However, with the use of complex dataset for predicting the presence or absence of the species, It is essential that feature extraction is important to generate optimal prediction that can affect the model accuracy and AUC score of the model simulation. In this paper, we integrated the Genetic Algorithm Optimization technique, which is popular for its excellent feature extraction technique, to enhance the predictive performance of the PRF Model. a novel hybrid algorithm the genetically optimized probabilistic random forest algorithm, designed for predicting the distribution of masto

SUBMITTER: Adekunle TA 

PROVIDER: S-EPMC12422458 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature