# Advancing Taxonomy with Machine Learning: A Hybrid Ensemble for Species and Genus Classification
## 1 Introduction  
Exploring biodiversity involves a complex and demanding process. It begins with extensive fieldwork, where entomologists venture into diverse and often remote habitats to gather specimens. These are subjected to rigorous identification procedures, including morphological assessments, genetic analyses, and taxonomic evaluations. This meticulous work is necessary for deepening our understanding of animal diversity, their ecological roles, evolutionary links, and interactions with human endeavors. For instance, it is important to highlight that insects play a critical role in ecosystems, such as pollination, decomposition, and serving as a food source for other organisms. This underscores the urgency of cataloging and preserving their diversity. In this paper, we mainly focus on insect datasets. Although an estimated 5.5 million insect species exist, only around 20% have been documented [1]. The challenge of documentation is intensified by the extinction of numerous species before they can be formally described [2]. In biology, taxonomy refers to the branch of science concerned with the conception, naming, and classification of groups of organisms. Modern classification divides any organism based on the Domain (Archaea, Bacteria, and Eucarya) and then on Kingdom, Phylum, Class, Order, Family, Genus, and Species. An example can be seen in Figure 1. Traditionally, taxonomists rely on morphological features to classify insects by their physical characteristics [3]. However, these keys are less effective for undescribed species with indistinct or missing diagnostic features. To address this limitation, DNA barcoding [4] serves as a complementary method, using genetic variation to identify species when phenotypic traits are insufficient [5]. Identifying insect species remains a major challenge. Although the DNA Barcode Database (BOLD) [6] holds a large amount of genetic data, most of it is not related to known species. This mismatch reveals the slow progress in identification, made worse by a shortage of taxonomists and a decline in traditional taxonomy practices, as explained in [7]. To address this, there is an urgent need for faster and more efficient methods to uncover and classify species. In this study, we tackle critical challenges in machine learning for insect classification, focusing on the issue of incomplete species representation. We propose an ensemble model designed to classify both known and unknown species. This model integrates traditional support vector machines (SVMs) with deep learning by converting conventional feature patterns into two-dimensional representations through vector-to-matrix reshaping, forming a three-channel input. The proposed approach combines DNA barcoding data with image-derived features to train SVM and the neural nets. Figure 1: Example of modern taxonomic categorization for Adelpha lorzae. The motivation for adopting this ensemble approach lies in leveraging the distinct strengths of both neural networks and SVM, making the system particularly effective for tasks that require diverse decision boundaries and robust performance against overfitting. Neural networks and SVMs utilize fundamentally different learning techniques, which often lead to different errors on different sets of samples. Neural networks reduce error through backpropagation across multiple layers, whereas SVMs focus on maximizing the margin between classes. By combining these complementary prediction strategies in an ensemble, the model benefits from the diverse decision boundaries created by each algorithm, ultimately improving overall performance.