Enhancing the Performance of Machine Learning Classifiers through Data Cleaning with Ensemble Confident Learning - Okabayashi Miyaji, Renato; Valencia de Almeida, Felipe; Luiz Pizzigatti Corrêa, Pedro (Journal of Information and Data Management, 2025)
Model-centric techniques, such as hyper parameter optimization and regularization, are commonly used in the literature to enhance the performance of Machine Learning Classifiers. However, when dealing with noisy data, Data-Centric approaches show promising potential. Thus, in this paper a new method is proposed: the Ensemble Confident Learning (ECL), which enhances the Confident Learning technique with the use of multiple learners to improve the selection of instances with biased labels. This method was applied for a case study of Species Distribution Modeling in the Amazon using Classifiers to estimate the probability of species occurrence based on environmental conditions. Compared to Confident Learning, ECL showed an improvement of 20% in Recall and 3.5% in ROC-AUC for Logistic Regression.