# Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification
## 2 Related Works  
Machine learning (ML) techniques present innovative solutions by analyzing complex data patterns to classify species and detect anomalies. Traditional ML approaches have shown promise in recognizing subtle morphological traits in images, including those of undescribed species, as shown in [9]. Although not yet as accurate as DNA-based methods, recent advancements indicate that image-based ML is approaching expert-level performance in entomological studies [10, 11, 12, 13, 14, 15]. However, these models are constrained by incomplete training datasets, which are particularly problematic when dealing with rare or undescribed species and the morphological variations that occur in different stages of insect life [16, 17].  
In recent years, many machine/deep learning approaches have been proposed for DNA barcoding classification [18, 19, 20]. In [21], the study examines two primary approaches to taxonomic classification: database-based methods and machine learning techniques. Database methods generally achieve higher accuracy when extensive reference data are available, whereas machine learning approaches perform better with limited datasets but tend to be less precise overall. The study also demonstrates that integrating multiple database-based methods enhances classification accuracy, offering valuable insights for computational biology. In [22], the authors introduce BayesANT, a Bayesian nonparametric taxonomic classifier designed to determine the taxonomic affiliation of DNA sequences, even for organisms without reference sequences or previously unidentified taxa. BayesANT utilizes species sampling model priors to detect unobserved taxa across different taxonomic levels, enabling flexible and probabilistic predictions. The algorithm was evaluated using Finnish arthropod data and exhibited high accuracy, particularly in identifying taxa absent from the training dataset. In [23], a novel deep learning approach is proposed, integrating Elastic Net-Stacked Autoencoder (EN-SAE) with Kernel Density Estimation (KDE), referred to as the ESK model. An experimental validation on three datasets confirms the effectiveness and superiority of ESK, demonstrating its capability to accurately classify fish from different families based on DNA barcode sequences. For a recent comparison of standard machine learning algorithms applied to species family classification using DNA barcodes, see [24].  
Deep learning, a subset of machine learning, has been utilized in various entomological fields, including pest detection, analyzing plant-insect interactions [25, 26, 27], environmental DNA (eDNA) [28] or invertebrates image classification [29, 30, 31]. However, these applications are often tailored to specific insect groups, limiting their broader applicability, as in [32]. A critical challenge for ML-based insect identification lies in addressing both described and undescribed species. Many current models operate under the assumption of a fully represented dataset, which is rarely the case. Additionally, these methods face significant difficulties in managing the vast number of insect species and distinguishing outliers within the highly diverse Insecta class (see [16]). Other recent approaches are as follows: [33], where the application of DNA barcoding data is proposed in image-based out-of-distribution detection in fine-grained taxa identification; and [34], where a novel framework is proposed to combine computer vision and bulk DNA metabarcoding specimen processing pipelines to improve the accuracy and taxonomic granularity of individual specimen classifications.