A Comparative Analysis of Clustering and Feature Extraction Methods for the Automated Construction of Bird Species Classification Datasets - Santos, Virgínia A.; Terasaka, Diego T.; Martins, Luiz E.; Oliveira, Allan G. de; Ventura, Thiago M. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2024: Anais do XII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2024.244709, 2024)
The identification of bird species enables the creation of machine learning models that can be employed for the non-invasive monitoring of bird populations. In this study, we present an advancement in the assisted automated creation of a training set for the classification of bird species, with a specific focus on species present in the Pantanal. Typically, this process is conducted manually, which is a highly time-consuming approach. In this phase, we propose comprehensive comparative testing to ascertain the optimal methodologies for feature extraction and clustering. Five clustering methods and four feature extraction models were subjected to testing. The results of our experiments demonstrate that the optimal method for the purpose of this work was hierarchical clustering, using BirdNET for feature extraction. This combination provided superior performance in classifying bird species for the assisted construction of training sets.